The COMPLETE Data Analyst Course That Gets You Hired

So you've taken courses, you've earned the certificates, and you're learning tools like SQL or Power BI. But when you get to a real data analyst job listing, you still don't feel ready to apply. So you keep jumping between roadmaps, adding more skills that you probably don't need, and wondering whether your projects, portfolios, interview preparation is actually good enough. So while you're still stuck guessing, you just waste more months learning things employers barely care about without building actual evidence that gets you hired. So over the next 5 hours, I'm gonna walk you through the complete path What to learn, what to ignore, what projects to build, and how to create a credible portfolio, and how to prepare for applications and interviews. By the end, you will have a clear transition plan and know exactly how to present yourself as a credible data analyst candidate instead of just collecting more certificates that you don't need and feeling more unprepared. So let's get started. Everything that you have been told about passing data analyst interviews is officially dead. All right, so the first major shift is something the interview prep industry wants you to ignore. You're spending hundreds of hours trying to. Write flawless code from memory. And you think that just mastering syntax, every comma, every parentheses, every semicolon will get you the job. And you think that your value as an analyst is directly tied to your ability to act as a human compiler. But here is a massive problem no one is talking about. AI has now made technical skills incredibly easy to fake. We are living in the era of the vibe coding crisis. Andrej Karpathy, the former director of AI at Tesla and the co-founder of OpenAI, coined the phrase vibe coding in early 2025. And described it as fully giving into the vibes and just forgetting that the code even exists. Just seeing stuff, saying stuff, running stuff, copying, pasting, and thinking that it mostly works. And when he said that, people thought it was a joke, but now it has become a crisis. According to GitHub, 41% of all new code written globally is now AI generated, and companies are paying for it the hard way. An analysis found that code churn, which is code written and And then immediately reverted or fixed within 2 weeks increased 39% in projects heavily using AI tools. Google Chrome's engineering lead gave it a name, comprehension debt. It accumulates the moment you ship code you didn't write and you don't fully understand. So people are copying, pasting from ChatGPT, throwing it in production, and hoping that it works. When a data pipeline breaks at 2 AM on a Friday, AI cannot jump on a troubleshooting call. Only a human with critical thinking can do that. So hiring managers know that a candidate can fake their way through a basic technical screen using AI. So while technical screens still exist, they are no longer the ultimate deciding factor for roles that actually pay well. What employers are now testing for is your logic, your judgment, and your ETL thinking. Basically knowing how to think and structure projects end to end. They want to know if you can understand why data is being transformed in a specific way, not just how to write SQL statements. They want to see your approach to messy and unstructured problems where the requirements are vague and the data is dirty because it always is. And the best way to really develop these skills is to stop doing isolated perfect scenario coding problems or data projects. LeetCode is not going to save you. I was reading a thread on Reddit where someone went from months of rejections to finally getting an offer. They said the switch was this. They stopped treating SQL rounds as a coding test and started mentioning assumptions, edge cases, trade-offs, and performance considerations in the interview. They focused on explaining their thinking. Not just trying to impress everybody. So that is a shift that you need to make. Before you write a single line of code on a messy dataset, write out the pseudocode. Write out the steps in plain English. When you're in the interview, talk through your thought process out loud. Say, here's a business problem as I understand. Here's how I would extract the data. This specific table usually has null values, XYZ. Prove your logic, prove your critical thinking, and prove that you are the human who governs the tools and fixes the mess when AI breaks something. But even if you can explain your logic perfectly, there is a new gatekeeper waiting for you because it is still easy to fake technical skills on a 30-minute Zoom call. Thanks to all the new cheating tools out there, even the highest paying companies are now leaning heavily on take-home assessments and business case presentations. Real take-home assessments from companies like Airbnb, Stripe, Roku are the new standard benchmark. One hiring manager put it bluntly in a Reddit thread: Some people interview really well but cannot do the job. Assessments have saved me from bad hires. The companies offering $100K, $120K, $150K aren't just handing you money just because you pass a 15-minute SQL quiz. I went through take-home assessments myself. They were always from the higher paying companies. And it's the same for my students. The companies that pay very, very well want to see how you perform in the wild, not how you perform with a countdown timer. Here's what I've seen over the years. You have one group that gets the take-home prompt, answers specific questions, and then dumps all their code in a spreadsheet and then sends it back and then waits for a rejection email 3 days later. But the other group treats it like a consulting engagement. They don't just answer the questions, they actually solve it like an actual business problem, and then they get the offer. One of my students landed a senior people analyst role at Roku, and the take-home project that Roku gave her was almost identical to a milestone project that we do inside my program. She didn't just send them blocks of SQL code. She sent a comprehensive solution. She identified trends in employee turnover, visualized the cost of that turnover, and presented an actionable recommendation directly to the HR leadership team. Proof is the product. Companies use these take-home assessments because it is the only way to really see that you can do the job. AI cannot sit in front of a VP of marketing and explain why customer churn is increasing based on a messy dataset. AI cannot defend its methodology when a stakeholder pushes back, and AI cannot build trust. So to really prepare for these take-home assessments, you first need to read prompts and identify the core business objective immediately. What problem are they actually trying to solve? Is it increasing revenue, cutting costs, Prove retention. If you don't know the business objective, your analysis is useless. Second, do not submit your Jupyter Notebook or SQL script alone. That is a bare minimum. Submit a slide deck or written memo that summarizes your findings for non-technical audiences. Your hiring manager might be technical, but your stakeholders that you'll be working with on the job are not. Show them that you can communicate with both. Third, anticipate their questions. If you found an anomaly in your data, explain why you think that it's. There and what you should do about it. Did you exclude it? Did you average it out? Why? Bridge the gap between raw data and business strategy and show them what it would be like to actually have you on their team. And this brings me to the single biggest reason people bomb the final round. You can have the perfect SQL query, write all the perfect code in the world, but if your data storytelling is terrible, you will fail, period. I know what you're thinking. I'm applying to be an analyst, not a public speaker. That's the exact mindset that keeps people stuck at entry-level, low-paying roles, or not even getting hired in the first place. A survey found that 57% of hiring managers now value communication and storytelling more than technical coding skills when evaluating candidates. And 76% of those human-centric abilities are simply difficult for AI to replicate. So these aren't soft skills. This is the marketing telling you what it's paying for. Data without a story is noise and useless. And even working data professionals still struggle with this. Saw another post on Reddit that said, Hey, pulling data and cleaning it and building dashboards they can do all day, but they struggle with the so what part. When it's time to present to stakeholders, they completely freeze. They just end up reading the numbers on a slide like a robot. This is exactly why people fail. They pull up the screen and just read numbers out loud. They just point to a bar chart and say, as you can see here, sales went up 10% in Q3. And then the stakeholders just sitting there thinking, so what? Every chart that you build, every bullet that you write, every tool that you mention has to pass one test. How does this make the business more money or save them time? If your visualization shows inventory costs are rising, the so what isn't just inventory costs are rising. It's what the company needs to do to negotiate vendor contracts immediately, or they lose $50,000 this quarter. If your dashboard shows customer retention dropping after month 3, the so what can be the marketing team needs to trigger a re-engagement email campaign at day 75 to save $100,000 in recurring revenue. Skills are the currency of freedom today, but communication is how you cash it in. So use this framework for every presentation, take-home, portfolio, real-world meeting, whatever. 1, state the problem. What is a business issue? Set the stage immediately. 2nd, show the data. Provide evidence that the problem even exists and keep visuals clean. Remove the clutter. 3, provide the insight. Explain why it's happening. This is where your analysis really shines. And then 4, give the recommendation. Tell the business exactly what to do and be prescriptive. One more thing that will instantly elevate your work. Never show a chart with a generic title. Sales by region tells me nothing. It forces me to do your job for you. Instead, title it West Region Sales Dropped 15% Due to Supply Chain Delays. Tell the stakeholder what to think before they even look at the graph. Guide their eyes to the truth. Now I want you to stop and really internalize this. Everyone online is telling you to learn more tools. Python, R, Snowflake, dbt, the newest AI plugin, this BI tool, that BI tool, that certificate, whatever. They keep you on a hamster wheel of continuous learning and consumption of tools. So you never actually feel ready to apply. But it's not about mastering tools. It is about mastering thinking in ambiguity. Tools will always change. The syntax will change. There's going to be a new AI advancement every single week. But a business's fundamental need for a human who can look at a chaotic situation, find the truth hidden in the data, and explain it simply to the people making that decision, that need is forever. You are not a human calculator. You are a problem solver. When you shift your mindset from, I need to know my code, to, I need to show them how I think, the entire interview process changes and you stop being a commodity that can be replaced by a cheaper candidate or an algorithm, and you become a trusted advisor. In this video, I'm going to walk through a step-by-step 12-week roadmap to become a data analyst. This is going to be in-depth. I'm going to give you stuff week by week. Also stick to the end because I'm going to show you how to build experience beyond the 12 weeks. If you're just graduating from college or whatever it is, maybe you don't have, you're not confident in the experience that you have. I'm going to pretty much give you a cheat code on how to eliminate not the excuse, but the limiting factor of I don't have the experience. Going to teach you how to build the skills, build the brand, and build the experience. All right. So let's get started. All right. So this method, or that I'm going to teach you, it's going to be an Elevate method. There's going to be 3, you know, technically 4 phases, building the right skills, building your brand, crafting your story. Out of the 12 weeks, 1 through 10 is all about building the skills. It really doesn't take that long to build a brand. Honestly, it's going to be focused on your portfolio, LinkedIn, and your resume, and learning how to craft a narrative to prepare for your interview. And then I'm going to talk about the fourth optional phase of building your experience: thinking like an analyst. All right, so I know I remember when I first started learning analytics, I would always like skip the boring parts of like thinking like an analyst and things like that, and get to the more exciting parts. But before you touch any software, you have to learn how to think like an analyst. The reality and thinking that oh. You know, data is just like crunching numbers in Excel, whatever, is not the case. It's really important to wrap your mind on what data is and like the life cycle of it, right? So just like anytime you see a person, you know, you're seeing a person who they are right now. You see me, but like there's an entire kind of life cycle way beyond it that made me who I am, that made you who you are. So the data that we see, it goes through a whole life cycle that made it through, you know, what it is. And so knowing that context is going to be super important whenever you're problem solving on the job, right? There's always stories and narratives beyond the numbers, right? So the data analyst lifecycle is something that you should understand. Defining the objective, then collecting the data, cleaning the data, analyzing, drawing conclusions, and reporting results. And this is just something to wrap your mind around. Just, you know, again, like the journey that data goes through from the raw sources all the way to the end, just knowing the idea behind How that is, right? If you take a course, you most likely will start with, hey, here's a CSV file, here's an Excel sheet, but there's an entire journey that that data goes through, you know, ideally before it gets to the spreadsheet. And just understanding that even on a basic level is like super, super important going forward. One thing you also want to familiarize yourself in this first week is key performance indicators, right? So when it comes down to benchmarks for measuring business performance, It's always going to tie into basically the 3 Rs of business. I mistakenly have the 4 Rs, but it's really 3. So the 3 Rs of business, managing resources. So you think about, you know, assets, think about retention. So retaining employees, retaining, you know, customers, assets such as like if you, you know, if you have trucks, you know, properties, et cetera, managing revenue, how do we increase sales, profit margin? And then managing risk, fraud. How do we design a system at scale? Do we have anything that's risking growth prevention? Do we have anything that is, uh, you know, risking our profit margins? All KPIs, whether you're in supply chain, you know, retail, wherever you are, ideally any KPIs, any metrics are really going to fall into wherever the, the 3 Rs of business is going to be, right? Managing resources, managing revenue, and managing risk. This is why I said it's super important to really dive into domain knowledge. So if you're a career changer, you already come with a domain knowledge ideally, and you already know, you know, the types of what success looks like and how things are measured and things like that. And let's say if you are just graduating out of college or you want to pivot industries, really understanding how success is measured. In that domain is especially important because key performance indicators, these are going to be things that may not necessarily be easily defined. These are going to be things that you're going to be doing meetings with stakeholders. You're going to be asking them how they define success. There's going to be a good chance, right? You're going to be creating a report for, you know, a report for a process that does not have a report. There is no measures and stuff on paper. So you'll just be talking to people and getting their frame of like how things are measured and things like that. You're probably going to have to, you know, create them yourself, right? But so understanding where these metrics and processes that you're going to be familiarizing yourself with, where they fall into those buckets of the Rs is going to be super important because that's how you're going to also help see where your work connects to the business's bottom line. Because remember, you're not just You know, reporting numbers and spreadsheets. Everything that you do has to attach to the bottom line because you're there for providing value, especially these days where companies want to get more lean. We're going to talk about AI in a minute, as you can see, but not necessarily to replace with AI, but there's, there's been a whole lot of bloat in companies and things like that. And when it comes down to just, you know, just showing your value, it's super important to understand where your work ties into any of the 3 Rs of business. Managing resources, managing revenue, and managing risk. Okay. Now again, for week 1, independent problem solving. So understanding the analytical lifecycle, analytics lifecycle, understanding KPIs. The next rollover to that is independent problem solving. It is more important than ever. I did a roadmap video a year ago, a year and a half ago, probably at this point. And one of the things that was not was pretty much an update of that is the downward trend pretty much of problem solving. The overreliance of AI has really made independent problem solving a more rare asset. When I was pivoting into analytics, the big differentiator honestly was storytelling, data storytelling, data visualization. There's a lot of people who they don't have like a creative side. So it was very easy to stand out. With data storytelling and biz. Now, same thing on top of that independent problem solving. What's happening is people are rushing too much to AI and making it their pilot and they're being a copilot. I say it all the time, you are the pilot and only use AI as your copilot. When we're in week one, don't even think about AI. Don't think about AI. Don't even use AI until way afterwards. You don't even need to think about it. Don't rush to it. But what's happening is. And there's been data on it is that there's multiple data, but I have this particular snippet of a, of an article where MIT did a study and it proved that AI is making us delusional. You know, you use ChatGPT and it's giving you answers. You know, you ask a question, the model is just agreeing with you. It's important to really focus on independent problem solving in this week and you're going to carry it over. Every, you know, to rest of the weeks. It is super important. Do not rush to AI. Look, don't even use GPT or whatever in these first few weeks. Like, just don't. And this also goes into what solving business problems actually is, right? Content creators online always talking about the same old cliché, you know, uh, hey, can you solve problems with data? Can you solve business problems with data? Using data to solve business problems. What the heck does that actually mean? Okay. We don't do a great job of like actually explaining what that means. So I'm just going to give a brief kind of perspective of what that means. What does solving problems in business or solving business problems actually look like? So here's the thing. If whenever you get like a spreadsheet, a report, or a dashboard that has like half the, half of the information that's needed to make a decision, right? You need data, i.e., the numbers, whatever it is that's collected, plus the context. You don't look at a number. You don't look at a metric. You don't look at a KPI and you don't look at a dashboard or report and then make a decision. decision there, you take it and, you know, induce questions, whatever it is, and next steps on to get more context behind the numbers, right? So there's a really good clip on Instagram. Jeff Bezos told a story of the early days of Amazon when their metrics and the context, you know, didn't match.

We had metrics that showed that our customers were waiting, I think, less than, I don't know, 60 seconds when they called a 1-800 number to get And, you know, phone customer service, but we had a lot of complaints that it was longer than that. If you have a bunch of customers complaining about something at the same time, you know, your metrics look like why they shouldn't be complaining, you should doubt the metrics. And so one day we're in a meeting and we get to this metric in the deck and the guy who leads customer service is defending the metric. And I said, okay, let's call. Picked up the phone and I dialed the 1-800 number and called customer service, and we just waited in silence for—

What did it turn out to be like?

Oh, it was really long. More than 10 minutes, I think.

Oh, wow. I

mean, it was, it was many minutes. We weren't measuring the right thing, and, and that, you know, set off a whole chain of events where we started measuring it right.

He explains, whenever the data and the anecdotes disagree, The anecdotes are usually right, right? If you have a bunch of customers complaining about something, your metrics look like they shouldn't be complaining, you should doubt the metrics.

When the data and the anecdotes disagree, the anecdotes are usually right. And it's usually not the data is being miscollected, it's usually that you're not measuring the right thing.

This is the reality of what business, uh, problem solving is constantly in, and this is why AI is limited. It is limited to that anecdotal side. You cannot, and you will not dump all of the anecdotal of whatever it is in an AI. It's just not happening. You have it all in you. And that's why you reason with the numbers. And that is a limitation and why you should completely ignore the nonsense that is spewing from the propaganda that basically AI is more intellectually superior than humans. It's not. Okay. I wanted to stress this because again, there's probably folks that are going to skip this week for, for like, oh, we don't want to do problem solving or whatever, blah, blah, blah. This is where you're the value of a data analyst is. And why, when it comes down to promotions, whenever you get promoted later on in your career, whether you want to be a manager, senior analyst, go into data engineering, analytics engineering, product management, whatever it is, this is going to be super important in this new job era because most people are using AI too heavily. Right? Their independent problem solving muscles are going to diminish, right? They're going to look at numbers, not really consider the context enough, and then provide very inaccurate reports and inaccurate whatever, you know? So super important. This is what business solving business problems actually is, right? Context plus data. I come from a Six Sigma Green Belt background, so I have learned nonstop problem solving frameworks. But one of my favorites is called DMAIC, define, measure, analyze, improve, and control. There's all types of stuff like, right, define, clarify the problem and goals, right? That's like something that we always talk about. Measure, collecting data on the current process. Analyze, identifying the root cause and defects. And then improve, right? Creating solutions. And then control is just like how to monitor performance over time. This is basically data analytics in a nutshell, right? There's some DMAIC projects that are very, very kind of strict, but the framework of DMAIC is something that I feel should be rooted for every data analyst. So I highly, highly encourage if you're watching this and you're interested in Six Sigma, I think Six Sigma is a huge competitive advantage if you use it right. I would definitely research and look into DMAIC. Again, there's tons of problem solving frameworks. Another one is called the 5 Whys. This is something where, you know, you're just asking Why 5 times to really get to the root cause? So again, it goes back to remember where the numbers and the anecdotes, where they will conflict. And that's, that's going to happen often, to be honest. If you're seeing, you know, hey, there's a drop in sales, why sales drop, you know, 5% this week. And there was a 50%, you know, increase in leads. You know, you asked why multiple times, right? And then you get to. You know, the root cause. Very rarely, that's why you don't make decisions when you look at a number or when you look at a dashboard or you look at an initial report because you're going to miss the underlying issue every single time. There's a bunch of problem solving frameworks, but choose the ones that you really like. I really like the MIG. I really like the 5 Whys. I personally am someone who always likes to be very simple and always tries to find the root cause of things and really try to work on the constraint first. That's just how I'm wired. Again, there's Tons of problem-solving frameworks. I would choose the top one or two that you really like and just like, you know, put them on a sticky note somewhere and that's something that's your go-to every single time you work on a project. That's week one. Week one, you ain't touching Excel or nothing. Not touching a tool. It's all about just learning about the data analytics lifecycle. It's all about problem solving. At end of week one, you should find, you know, one to two problem-solving frameworks that you really like that you will continuously use. Throughout the rest of the weeks, right? Whenever you're doing projects, whatever, you're going to utilize these frameworks. So, right, have 1 to 3, 1 to 5 in your back pocket so you can constantly practice. The idea about these, right, these, these sound good, like DMAIC and 5 Whys, but the skillset comes through repetition, right? Not just like reading it one time, watching a video of it one time, and then, you know, putting it on the shelf. It comes through repetition. And that's also one of the great things about this roadmap is that we're going to be pretty much You know, the following weeks are always going to pretty much build up on the next. Okay, so week 2. Week 2 is going to be all about statistics and Excel, right? So Excel is a fundamental tool of analytics. With this roadmap, I recommend learning data cleaning, statistics, data visualization, all in Excel. And then once you get to the other, you know, the next tools, you're just trying to get familiar with the tool, but not necessarily like the concepts of data cleaning and, and statistics and whatever it is. Right. And you can learn all those things we're going to learn in Excel because it's the easiest and the most familiar tool that most people have. Like literally, I've yet to meet anybody, any student, any person in the community over the past 5, 6 years at this point that has never touched Excel or Google Sheets. But anyway, when it comes down to Excel, it's all about, you know, pivot tables, lookup functions. Conditional formatting, and then data cleaning. And then you can also learn Power Query. The depth of Excel, the thing is, it's like you want to do like pivot tables is super essential, right? That's going to be like easy EDA, lookup functions, conditional formatting, and then learn descriptive statistics. The thing about it is that most companies, they don't have like the super advanced tech stack. A lot of companies still run on Excel. That's just how it is. That's why it's still the most popular tool. And there's going to be a lot of times where if you just want to do a quick analysis, Excel is perfectly fine. When it comes to descriptive statistics, this blends in also with the problem solving that I spoke to about in week 1. When it comes down to the numbers, right, you're gonna have to measure processes, right? That's what descriptive statistics are, right? You're gonna have to be using the mean, the median, the mode, most likely the average, you know, knowing the, the, the limitations of an average. When would you use a median, right? The, the use cases of why variance and standard deviations are helpful, why you would use a histogram, a box plot, things like that. Super important. It's all about measuring business processes, right? Metrics, KPIs, bar charts, you know, histograms, they are just an output of a measure. They are just measuring business processes. So the problem solving. The numbers, you're going to have to really hone in on descriptive statistics so you can understand the outliers and things like that and what they mean to the business. Because again, this is just half of the story. You have to have the context with it. Pro tip, again, I come from a Six Sigma background. I'm probably going to make a video on really why I feel like Six Sigma is like the GOAT when it comes down to being an analyst, especially these days. Learn process control charts. Very goated when it comes down to measuring business processes overall. Like after a while, like once you get more, you know, skill sets, you're, you're probably going to move on from doing data cleaning Excel most likely. But it's a great way to learn how to handle nulls, datetime, missing values, inconsistent formats, treating outliers, all these things you wanna do in Excel. So you're not like, you know, having to like fight with You know, a bunch of code and whatnot. And then once you learn that, apply it into— learn how to apply that into Power Query because Power Query has more automations. Yes, it's way more powerful than just regular Excel. Avoid VBA. You don't need VBA. I wouldn't even consider a job that says they want VBA. That, that any, any job that's still asking for VBA, that team is like way, way, way stuck in the past. All right. Week 3, we're going to get into story, data storytelling visualization. And in here, we're still going to be in Excel. Yes, we are not moving on to anything else. Learn data visualization in Excel. So the thing about data storytelling is that everyone in this before photo, we all do like really bad visualizations at first, right? And we learn over time how to create better charts. And, and better storytelling. The thing is, it's better to condense that learning curve as fast as possible. Learning to remove clutter, learning to how to use color with purpose, and learning how to direct the audience. I highly, highly, highly recommend reading Data Storytelling with Data this week. You can read it twice. It's a very short read. Doing that and then doing, you know, practicing, you know, storytelling and making like clean charts in Excel. It's all about removing clutter, using color with purpose, because anytime we, when you create a chart in a tool, Excel, Power BI, Tableau, like that default chart, it's never anything that should be ready for publishing. Like that is something that you're always going to have to remove things from the default charts. So recommended resources for this week alone, I highly recommend to read Data Storytelling with data. Please read that, read that. Even if it takes you like 2, 3 days and then you can spend the last, you know, couple days of the week just practicing in Excel. She also has an accompanied book, Storytelling with Data: Let's Practice. You can get both honestly. And she has actually like practice sets in there. So you can actually get both books and then like practice along with the book. Again, what most people do is they'll get the book, they'll read it once, and then it's like they'll forget about it. So just like with the problem-solving frameworks, it's not about reading something once, watching one video. You have to repeat. It's about repetition. So if you read the Storytelling with Data, right? Because I've seen people, I've seen their portfolios, and then I asked them if they read the book and they said yes, but their work, it makes it look like that's not the case, right? So knowledge is nothing without application. It's all about actually applying and repetition, right? So don't just read this once and then think that, okay, and just like forget about it. Like if you have to have it on your table, do it. Like I had Information Dashboard Design, which is a great book for designing dashboards. I used to always have it on my table anytime I would build dashboards at my first data job. Anytime before I build a dashboard, I'd always open it up. It also starts with like 13 mistakes of like building a dashboard. So stuff like that, like when it comes down to learning these skills and really mastering them, remove any expectation that you read it once or watch one video and then you're good. All right. Week 4, choose one. Doesn't matter which one. And just become highly proficient before even considering the other one. There's plenty of jobs for each, but don't be rushing to do Both at once. The main thing about learning Power BI and Tableau is knowing what good looks like. This is also accompanied with data visualization storytelling. We have to know what a good dashboard and a good, you know, chart looks like. And again, a big reason why a lot of us get stuck in this, you know, low level—I don't say low level—but like stuck in level one. when it comes down to data storytelling and visualization and our Tableau and Power BI skills is that we don't really understand what good looks like. So if without a standard, we're going to be spending months on the wrong things and we won't know if our work is really ready. It's like driving without, you know, rearview mirrors basically. So you really want to take time to study what's actually good. At first, don't go to Viz of the Day. Go to Tableau Public, go to Discover page, and then just search up business dashboards. Let's see just what like business dashboards look like. It'll look like something like this. Like it's going to be faster if we just know what good looks like and just reverse engineer, right? I think a big issue that I'm seeing when it comes down to that I've seen for the past few years is that it's like everyone wants like their handheld and things like that, which is fine. But ultimately a lot of success that we're going to get with being a great analyst is just seeing what good looks like and just reverse engineering it. To be honest, that's skill building in a lot of things, to be honest. Just knowing what good looks like, seeing what good looks like, seeing that standard and just working to reverse engineer it. Do not move on to SQL yet until you can make visuals like this, right? So again, storytelling with data, information dashboard design. This is a checkpoint. Visualization is such a big thing. I don't want you moving on to SQL until you can build work that looks like this. All right. So now we're getting into SQL. It is something that is still being tested on pretty heavily in interviews, whether it's verbally, like oral interviews or technical ones, or like, you know, actual like technical demonstrations. This first week you want to focus on like foundational SQL. Honestly, like how databases work. I'd say, you know, just pulling data, aggregations. I have it for week 5 and week 6, but honestly, however long this could take, sometimes this takes some people 3 weeks or whatever. Out of the 12 weeks, honestly, SQL is probably the one where it can take some people longer. Don't use the 2 weeks as like, if it takes you longer than 2 weeks to feel good about SQL, like there's something wrong with you. Like don't do it. Don't think about that. Sometimes it takes people like a month, which is fine. But by the end of these 2 weeks, you want to be comfortable with joins, inner join, left join. Don't even read about right join, like leave it alone. Window functions, CTEs, case when, aggregations, unions, and datetime manipulations. You really, really, really, really, really want to be comfortable with these. A big thing is practice. There's a lot of websites that you can practice like DataLemur, sqlpractice.com, pg-exercises. But again, this is something I have up here for 2 weeks, but this could honestly, you can spend about 2 to 3 weeks, 4 weeks, whatever it is. SQL is super, super important. And as you go forth in the rest of the, you know, the few weeks, SQL is something that's like easy to practice where you can just like go on these free practice sites and just like practice. Like, you know, I'm going to try to do 2 problems a day, whatever it is. All right. In week 7. Okay. So. I'm going to cover some of the types of interviews that you can get, but one of the types of interviews that you'll encounter are take-home assignments. I've had to do them. I've seen people have to do them. And one of the deliverables is a slide deck presentation. I have often seen people who have the most technical skills in the world, but when they provided their slide deck in the assessment, they didn't move forward. And it was clear why, because It was just not, you know, visually appealing. So it's very important to learn how to build slide decks, consulting style slide decks. I get that name from like the Big Four consulting, because that's like a very stereotypical, you know, industry of, you know, them making like these types of decks. So, you know, you can look again, look up Boston Consulting Group by far has probably the best style, a consistent style. So you can literally look at like Boston Consulting Group slide decks. Learn about the pyramid principle. Please do not treat, you know, PowerPoint as a place to just copy and paste, you know, your charts from Tableau or paste your charts from, you know, your EDA. It's, it's just not, that's just not cutting it, right? Week 8, this is where we're, you know, going to be really putting everything together. I'd say not necessarily this week. I'd say this week you want to identify 2 to 3 full portfolio projects, but this week you definitely want to complete one that demonstrates your ability to do everything that we introduced in the previous weeks, right? Real-world datasets. You can make them and, you know, make a project around the domain that you want to work in. And this week is something where you can also, what I recommend is like a 30-minute challenge method where you can have like mini projects for each day, like Monday, clean the data Tuesday. SQL practice or SQL challenge, you know, Wednesday, a data viz challenge, Thursday again, SQL challenge, accomplish something in the 30 minutes. That's the point. And that's probably better than trying to complete a full-blown portfolio project. It's good to get some small wins first and see yourself accomplishing things. And then you can go ahead and build a full-blown project the next week. Okay. Phase 2. The next couple of weeks, all about building our brand. Having the right skills is half the battle. You have to be able to showcase them effectively in this new job market, especially when we have a non-titled background and our background doesn't, our resume doesn't easily speak for itself until we do some tender loving care with them. So there's 3 pillars of our brand. There is a resume, the portfolio, and the LinkedIn. Most people, they're just going to focus on one. Of the 3 or 2 of the 3, you know, they may be strong in one, weak in the other 2. That's not cutting it. They all like build up on each other. Obviously, usually everyone knows about the resume, but very oftentimes you'll submit your resume and then the recruiter is going to look you up on LinkedIn. This is why the all 3 matters. One is not more important than the other. Like they all matter in the funnel. Of landing a role, so it's non-negotiable to not really highly prioritize all three. You can get these optimized all in the same week for your portfolio. I just recommend using Canva. Canva is super easy. You can freely host it. You can get your own website for free, and you can just copy and paste. There's no code. But again, you can get these all up and running in the same week. So the next week though, week ten, week ten, you can definitely spend more time on developing your portfolio. Let's talk about the anatomy of a winning portfolio. As you can see here. Your project title, your tech stack, you know, your key insights. And then you want to have screenshots of like, you can have the dashboard screenshots of some EDA. No one cares about your bunch of code. Please don't use GitHub. No one wants to go to a link to like an unorganized GitHub repo. This is an example of one of my students, you know, this is a snapshot of their portfolio in Canva. Like, as you can see, this is just, just, you know, text boxes and just pasting. Images, right, from, you know, their project, right? So this is super clean, right? So project title, tech stack, you can have images of it, key insights, visuals, dashboard screenshots, key charts. Well, no one cares to see your 20,000 lines of code. Small write-up. It can even be just like a sentence. It does not have to be like a huge thing. Okay, so these last couple weeks, we're going to talk about interview prep and crafting our story. What are interviews actually assessing, right? In the interviews, it's like, hey, can you do the job? How do you think? And will you deliver? Every answer that you, in a behavioral interview, needs to address at least one of these 3 things. If the interviewer is not feeling any one of these things, right? You're probably not going to get a, get a job. That's just how it is. Now, again, with AI, a lot of phonies out there just manufacturing a great tailored resume. And then they get to the interview and they bomb because they're basically catfishing. You know, they're not who they say they are. So be ready to, to be answering questions to show that you have the technical skills required. You know, how you think. I'm going to show, you know, a way to practice saying out loud how you think for SQL. That'll help in the next bit. This is why it circles back to the independent problem solving that I was talking about. Don't be rushing to ask them questions in a chatbot. You gotta be able to just naturally know how to do Independent problem solving. This thing here, how do you think? A lot of stuff on the job is going to be navigating ambiguity. And if it's obvious that like you can't really, you wouldn't do well with it, like you're going to really need your hand held. I'm telling you, it's not going to fly. Not in this job market. Obviously a culture fit. You know what I mean? This is, this is up in the air. You know, there's certain places that I wouldn't be a culture, I wasn't a culture fit at. If you're not a culture fit, you're not going to get hired. That's just how it is. That's not really something that, you know, you can really prepare for, to be honest. It is what it is. Number 2, how do you think is going to be really prevalent on the 3rd one? If people really like how you think and really approach ambiguity and things like that, higher, higher trust in your ability to deliver. Remember, hiring is very, very expensive and your goal in the interview is to reduce the risk. Right. Reduce the perception of risk in the hiring manager's mind. Right. And that's all about being clear, really having a great thinking process and being able to communicate that very well. That's why I'm saying independent problem solving is huge. You can literally ask questions to people to really like scope people out on that. So again, that's why it's super important not really to rush to AI. As you can see, the AI is not here once. It's not here once. It's for a reason. All right. Succeeding in technical interviews. When it comes down to succeeding in technical interviews, there's different types, right? Live SQL questions, asynchronous SQL questions. Think LeetCode, take-home projects. Like I said before, you can have the most technical skills in the world, but if you can't really present it well, usually it's going to be, hey, here's a dataset. Here's a prompt, build a dashboard or build a slide deck. Usually it's like a slide deck and some visuals. And if they don't like what they see, if it's not visually appealing, you will not be asked back to present. So take-home projects is really huge and it's something that's very common for higher paying jobs. But again, now with all the phonies with AI, companies are definitely getting more creative with how they are assessing technical ability. And a big way to assess honestly is just like oral assessments, just asking questions and just seeing how people respond without, you know, being able to consult with a chatbot. That's just, that's just the way it is, unfortunately. Again, my problem solving is like leg day. Under pressure, right? Under pressure being a job interview, whatever. We always default to our fundamentals and habits, which is why you really want to use these 12 weeks and really practice your independent problem solving like your life dependent on it, to be honest. All right. So I have a 4-step Live SQL framework for problem solving. Okay. So first step, you want to analyze the tables. Write out the expected output, like sketch out the result table, the resulting like columns, then build out your query starting with the WHERE clause, then the joins too, if there's going to be joins. And then think out loud, narrate your reasoning process out loud to the interviewer. This is part of being, having your thinking process evaluated. There's many times, even if you're like, 90% of your query is correct and it's like 10% wrong. Like you can still get passed through. That's why problem solving and knowing how to think out loud is super important. Very oftentimes, like it's not often, or it's not necessarily like super common. Like if you get like 3 questions and in order to pass, you have to have, be like 100% of the query has to be correct. Not, not necessarily. A great way to practice is just, again, I say practice how you play, right? You can record yourself doing this. If you're not in a bootcamp, you don't have coaching, whatever, highly recommend building that environment yourself and recording yourself. Right now I am using Tella to record this video. You can use Tella, you can use OBS, you can use whatever it is. Record yourself solving SQL problems. Keep it private. You know, stimulate that pressure so you can just talk through things and just again, build that comfort under pressure in the privacy of your own home or whatever, and just explain things. You can do the same thing of just like explaining concepts. This is a really good exercise, honestly, at the end of like each week as you're learning concepts. When you're learning CTEs, can you explain the data analytic lifecycle comfortably? Like just like in your own words to camera, and then just watch it like watching tape and just keep it private. This is why you can You know, consolidate a lot of the timeline when it comes down to a job trend or learning skills. It's not necessarily you need more hours of courses and more tutorials. This like feedback loops or these intentional practices, practice times, this is the stuff that's going to really speed it up. Common reasons for interview rejection, you know, lack of preparation, insufficient technical knowledge, right? The best way to really combat that is what I just talked about. Vague answers, not a culture fit. Listen, culture fit is, it could be up in the air. Sometimes not getting a job could be a blessing, to be honest, because not every place is a great fit. Not having the energy or enthusiasm, right? If you go, if you're showing up to the interview and it looks like you just woke up, you know, positioning yourself as entry level, you really have to position yourself. Not necessarily like an expert because no one likes a know-it-all. But you want to just have confidence, confidence in the midst of risk, basically. All right. Crafting the story. This is all about the STAR method, really having like your elevator pitches. So here, the idea is you don't want to position yourself at entry level. The best thing to do here is writing out stories. This is something that I have my students do when they first sign up. For my program, write out stories, like your high impact stories. Like you think of, you know, stuff where you were the superstar, you were the rock star, anything with leadership that really puts yourself in a great light that you want to share in an interview, write it down. Also create your 30-second elevator pitch. Everybody should have a 30-second elevator pitch. If you don't have it right now, this is something that I used to do whenever I used to like onboard students for our program. I would just ask them, hey, do you have an elevator pitch? No. Well, what would it be right now? 9 out of 10, everyone would freeze. So you always want to have an elevator pitch, like at all times. But this is going to help you in the interview. Weekly strategy, you want to have like a 30-minute method or 30-minute challenge method, right? So you don't always have to rush and build, like commit to these large, big projects that you never finish. 30 minutes. So like the week that you're learning data cleaning, you can do a data cleaning challenge for 30 minutes. You can do a 30-minute SQL challenges, whatever. Life happens and things like that. So this is also a good way to keep building the skills as time goes along without committing to these big arduous projects that we don't ever finish. Just 30 focused minutes a day. It's all about building our skills in a small, complete loop, right? So this is something that I created years ago for my Data Motion community when I just noticed like so many people like taking, like committing to all these big projects or committing to these courses and just never finishing. And then I would just, you know, send or create, like start these weekly project, weekly challenges of like SQL, data cleaning, data viz, data analysis, and things that people can just do in literally 30 minutes or up to an hour. And that really helped with building momentum, building the skillset in a smaller amount of time. Right? Because it's focus time, and you know your brain develops dopamine when you actually finished something. Okay, so that really completes like the twelve-week process. Now, an additional bonus, right? What's next? One of the biggest things I hear people say is like, oh, they don't have the experience. To become a data analyst, or it's like, oh, how can I get a job when I don't have experience? It's not a great job experience. So this is going to be a bonus. I'd say an optional step, right? If you, let's say you don't feel confident in your background, reach out to your local nonprofits, small businesses, go to your local chamber of commerce and offer free data analytics consulting engagement for like 30 days. And something where for free, don't be charging nothing. Most likely they'll have Excel sheets you can do data analysis on. At the end, you can, you know, do a dashboard, a slide deck presentation, and it's something that you can put on your resume and on your LinkedIn. It's actual, like, real experience that you can provide and say in an interview. And then you will also leave with references for, you know, applying. Nobody should be able to say, oh, they don't have experience. This is something that anybody can do. Everyone has local nonprofits and small businesses and folks in the Chamber of Commerce that would absolutely jump over a fence for free data analysis consulting. So that is what you do. Outreach to, you know, 10 a week, whatever it is, right? Until someone says yes, you can do multiple engagements. Each project can do something like, it can take you maybe like 2 hours a week. It doesn't have to be something that has to be like 10 hours or something like that. Most people are wasting time chasing the Google Data Analytics certificate and wondering why they're still not getting hired. They're told this is the first step to get into data. So they grind through all the modules, post the shiny badges on LinkedIn, and put it on their resume and just waiting for the job offers to come through. But then silence, no interviews, no responses, just being ghosted. I know the feeling. I was there. I followed the same playbook. I did everything right and I still couldn't land a single offer. And it wasn't until I stopped relying on certificates and started thinking like a real analyst that things finally clicked. So within 30 days of shifting my strategy, in-demand skill sets, high-value projects, and a clear portfolio, I had multiple interviews lined up and a few 6-figure offers on the table. Since then, I have helped over a thousand people do the same, from truck drivers to teachers to folks laid off with no clue how to pivot into data. And they're not just getting hired, they're skipping the entry-level roles and landing roles that change lives. So in this video, I'm going to expose the truth on why the Google cert isn't getting you hired, the truth that no one else is willing to share, and the exact things that you need to be doing instead. That will actually land you a data analyst job because the market is ruthless, but it is winnable if you play the game right. And no, you don't need another degree. You don't need to go back to school and you definitely don't need to go spend $10K on a bootcamp. You just need the right strategy. If this is your first time here, my name is Kedisha. I used to deliver pizzas and now I deliver insights as a self- taught data scientists, and I've helped thousands of other career changers break into tech without going back to school. And I'm here to teach you how to do the same. So let's dive in. So now you're probably thinking, why am I hating so much on the Google certificate? Let me break it down for you point by point. Because once you understand exactly where the certificate falls short, you'll stop blaming yourself and start seeing the game for what it really is. The problem isn't that you're not smart enough. The problem is the cert was never designed to get you hired in the first place. Let's dive into why. Number 1, it's not rigorous enough to compete. Why? Because the course doesn't really push you hard enough. You don't really get a lot of complex problems and you don't get feedback on your work. It's like preparing for a marathon by walking on a treadmill once a week. Technically you're training, but you won't be ready on race day. And when recruiters give you a technical screen, that gap becomes painfully obvious. Number 2, it doesn't go deep enough on the tools that actually matter. The certificate introduces key tools, but only on the surface level. You might touch some SQL and some dashboards, but not in a way that prepares you for real-world expectations. Because in actual data roles, you're expected to do more than just run basic queries and make pretty charts. You're expected to analyze, clean, visualize, and present data in a way that drives decisions. And when you're barely scratching the surface, the gap again becomes obvious, especially in interviews. These aren't nice-to-have skills. They're core to the job. Here's the final nail in the coffin. The Google cert teaches R as its core programming language, but most entry-level analyst roles don't even use R. They use SQL, maybe Python, especially at companies that pay 6 figures. So you're investing your time in a tool that might help you get into academia, but not in most business settings. It's like training with a typewriter in a world now full of laptops and computers, obsolete for what most companies need today. Let's be real, it's not a secret cert. You're competing with over 2 million other people who also completed the cert. And you know what happens when hiring managers see it listed by everyone? It becomes white noise. The harsh truth, the Google cert alone doesn't differentiate you. It just puts you in a crowd, not ahead of it. So now that we've dragged the Google cert through the mud, with good reason, let's talk about what really works. Because plenty of people are still breaking into data and you can too. So number 1, Build the right skills in the right order. Here's a harsh truth. Most people are learning data analytics completely backwards. They hop from Python tutorials to random SQL courses, sprinkle in some Excel, and wonder why nothing sticks. Let me save you some months of wasted time. You don't need to learn everything. You only need to learn the right skills in the right order and get very good at them. So number one, start with Excel. It's the gateway into analytics. You should be fluent in pivot tables, conditional formatting, some lookup functions, basic statistics, and Power Query for data cleaning. No, it's not just spreadsheets. Excel done right is actually a pretty good weapon. SQL. This is the real gatekeeper. If you can't write joins, subqueries, window functions, and clean data in SQL, you're not job ready. Full stop. Every decent data role expects this. And the Google cert doesn't really go into it in depth enough. Data storytelling. This is where 90% of your competition drops the ball. They can run a query, but they don't know what to do with the results. You need to be able to communicate the insights visually and clearly. Think, what's the big takeaway and why should anyone else care? Next, PowerPoint. That's right, PowerPoint. Not everything you do will live in a dashboard or a spreadsheet. Sometimes you'll need to present your findings in a clear strategic narrative. And if your slides look like copy and pasted bar charts with no structure or story, you'll be skipped over fast. You need to be able to design slide decks that influence decisions. Next, dashboarding with Tableau or Power BI. Just choose one. Learn how to build dashboards that not only look good, but answers real business questions. You need to learn layout, design, and how to lead your audience's eyes to insight. Lastly, an optional Python. Python's great after you've nailed all the above that I talked about. Focus on data cleaning, automation, and some light analysis, not deep machine learning. For most data analysts, Python is a cherry on top. Not the main dish. Remember, a good analyst doesn't just know tools. They know how to think. And if you can think like an analyst, present like a consultant, and execute like a pro, you're not just hireable. You've now become dangerous. All right. Next thing you need to do, build high-value projects that prove you can think like an analyst. So let's get one thing straight. If your portfolio is just a bunch of copy and paste projects from tutorials, you're not standing out. You're blending in, and that's the fastest way to stay unemployed. If you want to actually land interviews, your projects need to scream, hey, this person solves real business problems. When one of my students applied the exact same process I'm about to share with you, she landed 4 senior data analyst offers in just under 90 days. So here's how you do it. Number 1, research high value problems in your target industry. Don't just start with the data, start with the problem, because people are reviewing your portfolio. They don't care how many charts you can make. They care how you can move the needle. So ask yourself, what keeps decision makers up at night in your target industry? If you're targeting healthcare, maybe it's reducing patient wait times or preventing hospital readmissions. Maybe e-commerce, it might be customer churn or abandoned cart rates. Logistics, maybe it's supply chain delays or inventory management. Whatever the industry you're going after, your first job is to understand what matters most to that business. And look, you don't need to guess. You can literally Google this, hit Reddit, scroll LinkedIn, look at industry blogs, heck, even ask ChatGPT or Perplexity. What are 5 high value problems in SaaS marketing? Boom, you've got your problem worth solving. All right. So once you got your problem and you got your data, now it's time to tell a good story with your analysis. You're not just building some charts. That's just a baseline. Now you want to separate yourself from the pack. Ask yourself, what's the insight here? What's the recommendation? And if you were presenting this to a stakeholder, what decision would they make based on your analysis? And you're not just making visuals. You're driving action. So you need to deliver that story twice, once in a dashboard and then another in a consulting-style slide deck. Here's the difference. Your dashboard should give stakeholders a way to interact with the data. It should answer one core business question, be intuitive, and highlight what matters most. This is proof that you can build something clean, functional, and insightful. Insightful, not just pretty charts. Then you take that insight and package it into a slide deck, not just screenshots of charts thrown on the slide. I'm talking a clear narrative with action-oriented titles, business context, and then one big idea per slide. And then any recommendations that you would like to show as a consultant. Because real analysts don't just analyze, they persuade. They translate data into If your dashboard proves you just know tools, your slide deck proves you know business. And trust me, both are critical. So if you're not doing either of these, you're leaving a lot of money on the table. Okay, so once you have the skills, you got the projects, it's time to package it all in a portfolio that gets attention. This is your proof of work. This is what makes you differ from the 99% of people spamming the easy apply button. So when you put it all together, make it clean. You can host it on Notion, Canva, or even a free Wix site. It really doesn't matter. It just needs to be easy to access and easy to digest. As long as you don't send them to a random, unorganized GitHub repo. Because if I'm a hiring manager looking at your portfolio and I think this person looks like they're already doing the job, you're getting the interview, period. All right. So let's bring it home. If you've been relying on a certificate to carry your job search, now you know why that strategy isn't working. But more importantly, you know what also does work now. Learn the right skills and learn them in the right order. Build high-value projects that solve real business problems. You don't need another degree. You don't need to go back to school and you don't need to be stacking certificates. The reason you're 8 months into learning data and still don't have a job isn't because you're not smart enough. It's because you never had a real roadmap. You probably just had a random tutorial playlist, and I can prove it in the next few minutes by building the real thing for you from a blank screen live. Here's what no one's telling you. The internet is full of a thousand voices all screaming different things. Learn Python first. No, learn SQL first. Just network. And everyone ate a good So you're not stuck because you're lazy. You're just stuck because you're drowning in conflicting advice with no order to any of it. And when you follow that noise, you'll spend another year busy, broke, and still no job. So the next few minutes, I'm going to help you build a complete start-to-finish data career roadmap on this blank screen. The exact skills, the exact order, and the timeline to land a job in the next 4 to 6 months. And the last step is one that nobody includes. It's going to be the difference between I'm studying and I'm hiring. So don't click off before we get there. Okay. So when it comes down to pivoting in analytics, and I've seen this over the past 5+ years, there's really 3 main things that's really made everyone's path much harder and just way more slower than it needs to be. Number 1, shiny object syndrome, right? Some people, right, they see something online and they want to be an analyst. Then they see something online the next week and then they switch paths and want to be a data scientist. And they see something that they want to be like some AI engineer or whatever it is. Like, you gotta stop hopping between different paths, different tools, and different courses. You just have to stick with one thing. The next one, never deciding on a target and really just not having a plan, right? I just want to do data. I want to pivot into data. That's not a plan, right? You will never be successful. You'll never make the pivot if you do not stick with one thing. Number 3, they only build but never position, right? So these are people who, you know, they have all the skills in the world, right? But. When it comes down to positioning themselves, when it comes down to showing up with their brand, showing up with their resume, whatever it is, communication, they just get passed over. So these are the main, the biggest 3 reasons why I see people who they'll start the pivot and just never actually make it through or they're never successful. If you find yourself in any of these 3, stick it out. I'm going to show you exactly how to break out of these. Now, if you're new here, my name is Kedisha, self-taught data scientist turned instructor and data career coach. And I've helped thousands of other career changers break into analytics without going back to school. School, and if you ever want to work with me to land your first role in data, click the link in the description. All right, so let's get started. So when it comes down to pivoting into data analytics, making that career change, there's really 2 main forces that's going to determine your success. That is going to be positioning and discoverability. So positioning is all about, again, how you position yourself in front of decision makers, you know, hiring managers, recruiters, right? That is going to be your resume, your LinkedIn profile, Everything. How do you show up? Right? How do you also package your skills, your experience into your elevator pitch, into your value proposition? All of that stuff is about positioning, right? If you imagine, let's say we're in the store, right? We're looking for chips, pizza, whatever it is. There's stuff on the, you know, eye level in the supermarket. There's stuff way above at the top. And then there's stuff on the bottom. You know, most likely the top selling items or whatever it is going to be usually eye level, not stuff you're going to have to at the top where No way you could reach it or whatever it is. Positioning matters, right? Discoverability. So today in this job market, you need to be discoverable online. LinkedIn, right? The way you show up on LinkedIn. Recruiters live on LinkedIn all day long. What does your LinkedIn give off? Is it set up in a way that is attracting DMs to your profile when you sleep, basically? So positioning and discoverability, we're going to talk about those Now, before going any further, there's a huge misconception on really the job market right now, right? So in reality, there's a huge gap between the job market in terms of the applicants and the companies posting the jobs. Most of you are going to be the applicants and you're like, man, there's so much competition. There's not enough roles, whatever it is. But the companies, right? There's so much data out there that companies are actually having a tough time finding enough skilled workers for their job applications. Now that sounds crazy. You're like, hmm, what does that mean? Right now in the job market, there's a huge quality issue. There's a huge quantity of applicants, but thanks to, you know, ChatGPT and AI, everyone can pretty much fake their resume and things like that. There's not a lot of quality of job applicants. So again, while there may be this disillusion that, oh, there's too much competition, et cetera, there's actually Not that much competition, right? There's a huge skill gap of folks applying for jobs and the companies posting jobs. I'm going to show you exactly how you can pretty much close that gap so you can be on the other side. All right, quick pause. Now, if you want to land your first job in data analytics and avoid the lengthy mistakes that I did, click the link in the pinned comment where you can find the exact steps that my students use to land their job in data analytics without going back to school. All right, now let's get back to it. All right, so your roadmap from scratch, right? So there's basically going to be 3 steps plus a bonus, right? We're going to talk about foundational skills, really how to pick your target role and the core skills and demands. And then we're going to talk about a gap analysis. I'm going to talk you through my Elevate method. And then again, if you stick to the bonus, I'm going to show you how to basically triple your income in the first 5 years of data compared to everyone else. Okay, so number 1, step 1 is about identifying the foundational skills. So remember, too many people, they're not sure whether they want to be an analyst, a data scientist, or an engineer. They're always flipping through. If you're a career changer and you're trying to pivot into analytics, the most common entry point is going to be a data analyst. That's also going to be the most highest probable entry point is going to be a data analyst, right? Why? Because pretty much so many jobs, you're basically an analyst. Right? Every professional is an analyst. Every professional is a data analyst, right? Tools don't make the analyst. Unlike data scientists that, you know, involves much higher amounts of maths and machine learning and coding and data engineering. Those are not typical entry-level jobs, right? It's very easy to look up the salaries and say, oh wow, well, you make more money being an engineer and a scientist, but in reality, the data analyst is going to be your common entry point. And so that's what you definitely need to focus on. Focus on. And again, it's going to be the best if you have a non-technical background, right? Don't be swayed by too many folks who go to like these data scientists boot camps, et cetera. So many people go to these boot camps or get a master's in business analytics or data science degree and just think that they can take their non-technical background, get this degree or get this certificate, and then automatically they can step into ML engineer roles, et cetera. Too often I've seen people spend so much money on those paths only to realize that there's just too much of a gap in their experience. And then they feel like they have to settle for an analyst, but that's just not how it works. You know, it's, it is what it is. Data scientist, data engineer, more so of not necessarily entry level, but best if you specialize after you're an analyst, right? All right. Now, when it comes down to it, once you've identified, hey, whether you're going to be an analyst, A data scientist, a data engineer. You want to do a skill gap analysis, right? So let's talk about data analysts, right? Because that's going to be the most common entry point if you're a career changer into data. You look at what are the skills that are necessary, right? Excel, SQL, Tableau, data storytelling, whatever it is, Python. Then you give an audit. Audit yourself, rate yourself. 0, you never used it before. You know, 1, you've been exposed to it. You know how to use it a little bit. 3, you know, you can. Do say like a project with that skill on your own. And 4, you know, you can teach people that skill, right? Audit yourself, grade yourself on it. And at the end, you'll be able to see, you know, what skills you're missing, right? So you want to give a good, honest skill gap analysis so that you don't go off and waste time, you know, taking courses, et cetera, that you probably don't need. Whatever you can kind of focus on exactly what you need to Do first. Okay, so once you identify that skill gap analysis, then you can take focused course. You can do focused courses on exactly what you're missing. So if you realize that your skill gap is very low on SQL, for example, but very high on Excel, you know, maybe do a very quick project in Excel, but you want to spend more time during your week on SQL, for example. Maybe you realize that your SQL, you know, you're exposed to it a little bit, but you're not at the level where you can teach someone. CTEs or window functions. Rather than spend a whole bunch of time on maybe, you know, the skills that you're already well equipped with, you can focus your limited time, right? We all have a finite amount of time. Focus on just, you know, those core things of the skills that you're missing basically, or that you're lacking. Of all of these though, Excel, SQL, Power BI, Tableau, you want to have some sort of artifact or evidence of these skills. And we're going to talk about this in the next one. All right. So we're going to step into the ELEVATE method. So after we've done our first couple steps, which is identifying our target role, whether it's data analyst, data engineer, data scientist, then we're going to, you know, identify the skills necessary for that role. We're going to, we did our gap analysis to see of the skills that we have, what's our skill level? What's our, you know, level of ability? And then we see the gap of the skills that we don't have, the skills that we need. All right. And we've taken courses, we've done the coursework to train up, you know, what we need. Then we're going to get into this ELEVATE method here. Okay. So I talked about those skills, right? We need to have some sort of artifact or proof and evidence that we know Excel, that we know SQL, that we know Tableau, et cetera. And we want to build high value projects about those using those tools. Now, most people, they'll go on say Kaggle or whatever, or they'll go on YouTube and just do some, you know, follow along tutorial and then slap that portfolio project on their portfolio. Do not do that, right? Today, what you want to do is build your portfolio projects around high value problems in your target domain. So number one, there's going to be 3 different types of high value problems that you'll, you'll look at. There's data-centric, right? So what are these common challenges that data teams face? It can be, you know, No process or standardization, very weak data governance, you know, dashboards that no one uses. There's industry-centric high-value problems, right? Like supply chain inventory optimization, patient outcomes in healthcare, you know, conversion and churn issues in retail. And then there's organizational-centric problems, right? These are problems within a company. So if you want to target, say, like a FAANG company, those public companies that have, you know, 10-K reports that you can look at, you can look at you know, issues and problems basically about that company. For most of us who want to pretty much build a high value portfolio and high value projects around a high value problem, industry-centric is going to be our go-to, right? So have your target or define a target industry or 2, ideally whatever industry you're currently in or pivoting from, because you already have that domain expertise. And what you'll do is I've already made some YouTube videos around this, you can use AI, ideally, you know, a Manus, something with an agentic capability. You can have it source real datasets and create an actual project for you around high-value problems in your target industry, right? So I did a project or did a video on doing that with supply chain, way better than Kaggle. Most people are going to just say, let me go to Kaggle and get some, you know, whatever is on there. Avoid Kaggle. The thing about Kaggle is nowadays anyone can just upload anything from Kaggle. What you want to do is you don't want squeaky clean data. You want something very specific. You want something that has like multiple tables, all of that. And you want it real and you want it very focused on high value problems in your specific domain. Let AI do the legwork. You want to build your whole portfolio project on, you know, inventory optimization, supply chain, or Or whatever it is. Now, again, creating your evidence. So once you've done those projects, right? And again, you don't need 10. You just need about 3 to 5 strong ones. If you're going to do them like what I just did, just 3 of them is completely fine. You need to package the best ones in a portfolio. I am a big fan of one-pagers, no-code one-pagers. Canva is my go-to. And your portfolio needs to do 4 things. It needs to be easy accessible, right? Just a simple, you know, URL that you can link on your LinkedIn, that you can link on your resume. What most people don't realize, they will link something that, you know, needs a password or you need to log into something. And if no one can see it, then no one can access it. So easily accessible, easy to navigate. I like no-code one-pagers where you can just scroll down or you can just see everything. No need to click around or no need to do anything like that. Worst case scenario is people using unorganized GitHub repos. I would avoid GitHub repos altogether. You don't want anything where people have to like look around or not really sure where to find things. And it needs to be visually appealing, right? This kind of goes without saying. The more charts and visuals with very, very good storytelling, the better. And then easy to consume, right? Use a 10-second rule. Can someone grasp what your project is about, you know, in 10 seconds? For every project, Right? Don't just paste, you know, a bunch of code, 20 lines of code. No one wants to necessarily see that. It's like a snippet of code, right? You can use carbon.sh, but it needs to be very visually appealing with visuals. So you can use consulting style principles for your, you know, charts and things like that. Again, quality over quantity. At this point, right, we're in step 2 of our Elevate method. We got to build our brand. So again, it doesn't matter if we are the best You know, we have the most SQL skills. We have the most Excel. We're the best of the best. If we don't know how to position ourselves in a way that we're discoverable, none of that matters. Okay. None of it. I know we've been in situations where we've seen people with less talent, half the talent that we do, getting the promotions, getting the jobs. Okay. It really comes down to the brand, right? Your brand has 3 assets. Most people only focus on one, right? It's your portfolio, your resume, and your LinkedIn. All 3, they all work together. Nowadays, I'm telling you, anyone I've seen, there's job applications that will ask you, hey, do you have a URL or a portfolio? They will ask you for your LinkedIn. Even if they don't ask you for your LinkedIn, I guarantee you the recruiter or hiring manager will look you up on LinkedIn. So all 3 work together. Most people just focus on their resume, but you can't just focus on your resume and have like nothing on your LinkedIn, et cetera. You have to have all 3 work for you. So I want you to do also a 20-minute brand exercise. And this is also going to play into step 3, right? I want you to think about, because it's one thing to just say, oh, your LinkedIn, your resume, things like that. But you have to know what your brand is about. What problems do you solve? Take a step and think about what 3 to 5 problems do you solve? What stories from your past do you have where you've solved those problems? And what are 5 character traits that you want the hiring manager to know about You want to have a story for each of those character traits. Okay. And you got to make sure you tie it all back to your target role. If you take some time to do this, I just guarantee you it's going to be way 20 times easier to craft your resume, craft your LinkedIn, craft your portfolio. And also it's going to lead to our next step. And that's crafting your story. The previous step, I told you to create those stories, really think about them because too many people will get the interview. And just bomb the interviews, right? Because they don't know how to tell their stories well. They will position themselves as entry level. They think that their stories aren't that interesting, or they don't spend enough time to really craft their stories, right? Think about your transferable skills, right? Think about your soft skills and really think about and craft your high impact stories to tell in your interviews. You can tell the same stories in your interviews. It's not like these employers and all these jobs are, there's a database to to say, oh, you know, you said that last week in another company. Now understand too, once you get to the interview, that company or that hiring manager, they are completely fine with your experience. Very often we've gotten, you know, rejections, rejection emails after an interview. They're saying that, okay, well, we were looking for someone with more experience. That means you did not have a great story to tell. Basically, that means you failed this, you know, part 3 of the Elevate Method. Because again, they can see your experience on your resume. But if you get there and again, you downplay your stories, you don't have the high impact stories. You don't flesh them out enough to where you are the star. You don't say we did this, we did that. You are the star and you really focus on the results. You're not going to do very well. You're going to bomb the interview. So again, crafting your story is a big thing. This is something that I highly, highly recommend taking the time, really thinking back on your career path. What are your high impact stories that you can tell in basically any interview? You definitely always want to flesh those out. Okay. So that's pretty much step by step on how to craft your own data career path. Now I'm going to put some money in your pocket, right? I'm going to show you basically the secrets that accelerated my career. Basically that put over $350K in my pocket in W-2 money in the first few years of being an analyst. I want to show you exactly how to grow your career faster than anyone else and earn more than anyone else in your first few years. And it's all about being, learning how to be a great consultant. Everyone's a consultant. You're either an internal consultant or an external consultant, i.e., McKinsey, BCG, PwC, et cetera. But you're an internal consultant, right? Your stakeholders, you're consulting them, but you have to learn the art of consulting. It's not about just being good at skills. It's not about just creating pivot tables. It's not about creating dashboards. So there's always going to be 100 things to do in a business. And I highly, highly encourage you to always try to find the high value projects. There's always 100 things to do in a business. And you always want to find, again, the high value stuff. And the way to do it is basically a bottleneck analysis. One thing I like to call it is the lever method. And the lever method is literally all about, again, defining what is the bottleneck, how to attack it, how to exploit it, and move forward. Again, there's 100 things you can do in a business, but if you identify the bottleneck and you adjust that lever, you focus your concentration on that, you're going to have the most effect on the bottom line than any other thing. It's just like the 80/20 rule or the Pareto principle, right? You always want to identify what is that 20% that affects the 80%. And again, if you think about a business, right? And this is just an example. A business can be lead flow. It can be conversion. It could be operations. It could be retention. Again, there's so many things, so many levers, but you want to identify how do I identify a couple levers that I can pull out of the 100 that will affect the most on the bottom line. And this is a version of it, right? You want to map the flow. So map the, you know, process, do a process map, identify what that constraint is, exploit that lever, create systems and create, you know, new methods and methodologies, create systems for the team to continuously, again, you don't ever want to go backwards. And then just elevate and repeat. Here's a cheat code, right? So again, identifying the bottlenecks. Once you think of like, hey, what is the constraint of this team? What is the constraint of this business? What is the constraint of the process? And then you want to focus your energy on there. That is the ultimate cheat code. So in your interviews, I want you to start using this in your next interview, right? When they say, hey, do you have any questions? I want you to ask them, right? If you look at your team right now, what's the single biggest constraint holding back your throughput? And if I join, what's the one lever you'd like me to adjust first to move the bottom line? That's going to change the whole interview, right? That is a high leverage question. And that instantly shifts you from just a regular candidate that asks the same vanilla questions to someone that's thinking like a consultant. It signals that consultant mindset. It signals that you want to work on the high value projects that really moves the needle. It's going to make you memorable because no one else is going to do Okay. So once you get your first job, right, this is going to be your pretty much 30, 60, 90 day playbook. So in your first month, right, you know, you're getting used to everything, whatever, you know, this may even take 60 days, but you definitely want to audit, right? You want to be able to create process maps or process flows of everything that you're learning, right? You're learning, you're going to be learning the business processes and you want to be able to literally draw, do create process maps of everything that you're learning. And once you do that, you will visually see, be able to identify the bottlenecks. It's going to be visual, right? Once you're mapping everything out visually, and then you can present that to leadership and you can propose, you know, targets. And then, you know, based on what they say, you can pretty much execute on all those things, right? That's going to, you're going to surpass everyone and just really pretty much position yourself as a consultant, but also someone who is not just waiting just to take tickets. The money in data is not in the first job. The money is going to be in the promotion and beyond that. This is the career leverage ladder. The higher you climb, the more impact, and the more impact, the highest leverage and value, and the more higher leverage and value you have, the more money you can make. You can literally build these skills that I see, like you can literally become like an internal consultant and have that strategic impact at your first role. Now, the salary may not necessarily, um, Follow that, right? Because it's your first job. You're not going to say, what's the promotions when it comes down to the amount you can make? You know, you can be at, you know, that first role for let's say 12 months or 18 months or whatever it is. And you can literally move up this ladder. Your salary may not, and most likely will not reflect that because, you know, when it comes down to just any role, when it comes down to promotion, what's usually the yearly promotion amount? What's just like You know, 5% or things like that. The real money is going to be taking what you've done and learned and going to another company, right? That's going to be where the money is. I had a student who his first job was like $75K. He literally was there for maybe 18 months. He did all, he rose up the ladder. He stayed there a little too long if you ask me. His second job or promotion in his career ladder, he doubled his income, right? But you definitely want to be moving up this ladder. And thinking about it, right? High leverage projects, high value projects, consulting. You always want to think like a consultant and the money will follow, right? Don't just be someone that executes tasks. That's not where the money is going to be. Okay. Again, so this is your data career roadmap. Identify your target role. Identify the skills that's needed for the role. Do your skill gap analysis. And then you create your curriculum and your own curriculum, your personalized like courses or whatever it is around that. Skill gap analysis, and then you do the Elevate Method, and then you create more money in your pocket focusing on being a great consultant. And that's going to be how you can pretty much 3x your income in the first few years faster than everyone else. Okay. So listen, I gave you pretty much everything you need to build your own roadmap. Now go build it. Most people think it takes months to master SQL, but that's because they're learning it the wrong way. In this video, I'm going to give you the exact roadmap that I have used to coach beginners from not knowing what the heck SQL is to analyzing actual business data with SQL in just 5 days. This is the exact framework that I have used to coach my students to land roles at companies such as Roku, Microsoft, Zendesk, and more. So if you're ready to skip the fluff and learn the SQL skills that'll actually get you hired, stick around for this video. Okay, so day 1, we want to get comfortable with basic SQL and joins. So SELECT FROM WHERE GROUP BY aggregations, COUNT, SUM, AVERAGE, right? As an analyst, we're constantly going to be using aggregations, doing analysis, KPIs. So you definitely want to be comfortable with aggregations. And then you want to be comfortable with joins. So our data that we need will be spread across multiple tables. So you need and must be comfortable with joining tables before you really move on to more advanced heavy SQL. There's just a lot of things you're not going to really truly understand if you don't understand joins. So again, basics, SELECT FROM WHERE, GROUP BY, aggregations, and then LEFT JOIN, INNER JOIN, and then expose yourself to UNION. This is a good practice that you can do for about 2 hours. So moving on to day 2, we're going to get into some more advanced filtering, and then we can be getting into string functions, datetime manipulation, and then filtering with wildcards with like, in, between, and not. Right, we're going to always going to have data. A lot of times we'll have data over time, so that's why it's good to have datetime manipulation. And then you could also create different features with your dates. So you definitely want to be comfortable with datetime manipulation. The functions can differ between different types of relational database platforms. So definitely want to be cognizant of that. But this is a good setup for day 2, and this can probably take you about an hour or so. All right, so day 5, advanced joins and then subqueries. So now we're going to get into a bit more intermediate advanced SQL. And so we're going to introduce a couple other joins, self joins and cross joins. The first set of joins, left join, inner join, you'll be doing those most, but you want to be able to identify or be able to identify, say, like hierarchical data to where you would be using a self-join, for example. And then you want to be getting into subqueries. So there's going to be times where you'll have to be starting to use more advanced manipulation in SQL. This is where subqueries will come in. Being exposed to or understanding exists Not exists. Okay, so day four window functions. You want to be comfortable with window functions. Now this is this is at the point where we're at advanced SQL. So rank, dense rank, row number, lead, lag. Very comfortable with window functions. You don't want to be applying for interview if you're not comfortable with window functions. All right, so day five final day common table expressions or CTEs. You want to be comfortable with chaining multiple CTEs, being able to understand the difference between— the differences between CTEs and subqueries, understand when to use one over the other. Some people can use them interchangeably. Some people prefer one or the other. You just want to make sure you understand what is a CTE, what is a subquery, when is the best time to use one, when is the best time to use a subquery. It's perfectly fine if you're not grasping these more advanced concepts as quickly as, say, the more junior concepts, which is fine. Just spend a little bit more time and spend a little bit more practice. Okay, so what not to learn. So avoid spending your time on update, alter table, delete. These are going to be permissions that in the real world, us as data analysts, we're not going to have these permissions to utilize anyways. So don't really bother or really spend your time on these. And just double down on the concepts that I spoke about earlier in this video. It was just going to save you time. You want to be efficient, right? You want to learn concepts and then spend a lot of time practicing and not spending time learning and practicing concepts that you don't need. I got really good at SQL in just a few weeks, and I did almost none of the things everyone else tells you to do. No 50-hour course, no 200 LeetCode problems. I'm going to show you the exact unfair shortcut. On my screen right now. And once you see it, you can't unsee it. Here's why you're probably stuck. You've been told the way to learn SQL is to grind through tutorial after tutorial, watch someone else type, and then you'll forget it all by next Tuesday. That is a slow way. It may feel productive, but you can do that for a year and still fail your technical interviews. So in the next few minutes, I'm going to walk you through the exact unfair strategy, the thing that I did instead of tutorials that made SQL actually stick. It's one core strategy that will take your SQL skills up 10 levels in literally a week. So let's get started. All right, so most aspiring analysts are not that great with SQL, but you have to get good at SQL to land a data analyst job because it is still one of the biggest gatekeepers in interviews. I used to be one of those that was not very good, then I became so good that I now even teach SQL. So I'm actually going to share with you today the unlock that took me from a SQL noob that was failing mock interviews to acing the interviews and Also now teaching SQL. Okay, so the background of this is that I actually did a mock interview with an Amazon hiring manager. Thought I knew SQL at the time, but I pretty much failed it with flying colors. I pretty much exposed the fact that I only knew advanced SQL on the surface level, right? As we all like learn SQL, right? Very easy to get the basics down, like SELECT FROM WHERE, whatever. But once we get into the, you know, more advanced joins and the CTEs and things like that, things slow down, right? So I knew it at the surface level. But in that interview, I was put in an environment that I had never experienced before. And it pretty much exposed all the gaps in my SQL knowledge. And after that, you know, I did feel an embarrassment, but I really wanted to figure out how the heck can I close this huge gap in my SQL knowledge. So one thing that I learned after that day was that Using SQL on the job versus passing SQL interview are 2 different things. So whenever we're in an environment, like a coding environment where it's, you know, in MySQL and we have like a code editor and we see all the tables and things like that, it's different than when we're tested on the interview. There's going to be different forms of it. Doing SQL like on the interview is a completely different environment. And if we don't create that environment ourselves, we're always going to just bomb. On the SQL interviews, even if we feel comfortable whenever we're taking the courses again, it's a completely different environment for the interview assessments, and we're never going to pass them unless we learn how to create that environment ourselves. All right, so the tutorial trap when it comes to SQL, and we've all been there, and this is something that I realized after that failed mock interview, right? I know we've taken or we've used those SQL practice websites, right? You know, they'll have autocomplete, or you know, we can see what the answer is, right? We'll have errors that will flag real time. We can see the expected output. That's just not how interviews actually work. And we get into this trap of SQL recognition, but not thinking in SQL. So after that failed mock interview, I was like, how can I close the gap and learn how to think in code rather than try to just memorize things? But how can I think in code so that whenever I'm faced with any problem or challenge, I can be able to pretty much have success. And again, this is what pretty much the differences are. When it comes down to working always in a SQL editor, we're always, we're just practicing recognition, right? The autocompletes and everything like that. But in a real interview, we have to pretty much practice our thinking in code. And if we don't create the environment before the interview that forces us to think in code, we're never going to pass the interviews. So the unlock is I'm going to teach you How to create that environment or how to force yourself to think in code, think in SQL code, so you can ace any SQL interview. So right now, here's an honest test. In plain English, can you verbally say why you would use a CTE instead of a subquery? Or can you verbally say the differences between the two? Why you would choose one over the other? Not the syntax, but the actual reasoning. If there was any pause in your answer or you wasn't really sure, you feel like you have to go to ChatGPT or whatever. You already know what I'm going to point at, right? It's not your intelligence, right? We haven't really learned how to think in SQL code yet to just say that in plain English. So the fix is what I call the whiteboard method, right? There's going to be 3 parts to it. There's going to be what I call a no-fly zone. There's going to be voicing out loud and a skeleton before the syntax. The whiteboard method is literally what took me from surface-level SQL to knowing how to think in SQL code and being able to ace any assessment. So the whiteboard method is literally what it is in plain English. This is just a screenshot of one of the shorts that I have on my channel where you literally have a Word document or a notepad. You'll have a question, you know, a picture of the tables, and then you literally have to write the code or your SQL code on the Word document. There's no code editor. There's no, you know, database. You literally have just screenshot of tables, a question, and you have to force yourself to write your code on the Word document. That sounds pretty darn scary when you think about it, but I'm going to show you exactly why and how to do it yourself. All right, quick pause. Now, if you want to land your first job in data analytics and avoid the lengthy mistakes that I did, click the link in the pinned comment where you can find the exact steps that my students use to land their job in data analytics without going back. To school. All right, now let's get back to it. So the first component of the whiteboard method is the no-fly zone, right? So before your session starts, you gotta commit to nothing external. You know, there's no SQL error, you can't look stuff up on AI tools, nothing. It's completely on your own, okay? And just a reminder, this is what you do when you're trying to practice. So you don't do this like, you know, when you're, you know, learning first. This is for practicing for the interview. Secondly, right, when you are faced with, you know, having to, you know, do the whiteboard questions, right, you voice your thinking out loud. So this part is going to be pretty foreign because whenever we're taking courses or anything like that, we're not talking out loud, but you want to voice your thinking out loud while you are answering the question. This is what you would do in the SQL interview assessment or any interview assessment, any technical interview assessment. You don't want to be silent. You want to voice your thinking out loud. Describe your logic in plain language so the interviewer can understand where you're going, right? Know that you're not lost and can kind of, you know, what you're thinking. So this part is skeleton for the syntax. So just like previously where I said you want to voice your thinking out loud, you want to basically write out the steps of what the query would be in pseudocode. So plain language, right? So for example, the question says, hey, pull the customers who purchased after June 2024, right? So before you even start on like, oh, I'm going to select from where, just write out in plain language the steps that you're going to do, right? And say that out loud. You know, like, okay, I'm going to need to isolate records from last I'm going to need to connect to the customer table. If you learn this right in plain language, like pseudocode, it almost also gives you like a navigation because then you're going to translate that into your SQL code, right? So most people just don't do this in general, but rather than just rushing to, okay, let me just get to the SQL code first. No, if you write it in plain language and then convert that plain language into SQL code, it'll make everything so much smoother. So you're not, you know, trying to figure out the logic and the syntax at the same time. You just write the logic first. You solve the logic part first, and then you write the syntax, right? So you don't have to do it at the same time. Okay. So why this works is the goal is not about the problem that you solve. It's about the conditions of which you solve them, right? Again, people who are professional cyclists, right? They're not practicing with training wheels. You're not going to win the Tour de France if you're practicing with training wheels. So again, it's about the conditions that you're under. So in order to pass interviews, right, technical interviews, you need to create those conditions yourself and practice under those conditions. You don't wait till the interview to be under those conditions for the first time, right? That's where overwhelm comes. Learn how to— you practice how you play. That is what the whiteboard method is about. Now, some of you may be saying that, oh, maybe you've done interviews and you've never seen this before. The great thing is, if you can do whiteboard SQL, you can pretty much do anything. It's almost like running with a weighted vest. It'll be easier once you take it off. But if you can do whiteboard SQL, any SQL interview is going to be easy. That is the point. That is a huge point of it. Now, some of you may be thinking, okay, well, AI writes SQL now. AI gets stuff wrong. Okay. AI gets stuff wrong and you still need to be able to Direct the AI. Okay. AI is not going to know the right questions to ask or anything like that. AI can maybe make things easier, but it's not going to do the work for you. Okay. So remember that if you don't know the right logic to tell the AI or to instruct the AI or whatever it is, then AI is no good use to you. That's why you still need to know the logic. Okay. So your 30-day challenge is I want you to create your own whiteboard SQL challenges. If you can do whiteboard SQL, I'm telling you, you're going to be able to pass Any SQL interview. You can use AI to create your own, right? You can use AI to also grade it for you. This will force you to learn how to think in code so that whatever challenge on an interview that you ever get, it's going to be easy peasy. Absolutely. All right. So listen, you got the unlock. Go do this right now. Okay. Go do this right now. And please drop a comment on the biggest takeaways and also even DM me on LinkedIn if this helped you a lot. Most data analysts waste their time learning Python the wrong way. They sign up for some 50-hour course, start at lesson 1, and then they try to learn the entire language before they even touch a real problem. I did the exact same thing when I started and almost quit. So today I'm going to show you the unfair shortcut that I used instead. You will learn exactly why the learn everything first approach keeps you stuck, and you'll have a simple method that I use to learn only the Python that I actually need for the job and skip 80% of everything else. This works whether you've never touched a line of code or you've already burned through 3 courses that you've never finished and still like you can't build anything real. All right, so let me start by showing you the trap because you're probably standing in it right now. When most people decide to learn Python, the first thing they do is they go find a course on Udemy, YouTube, whatever pops up first. And then they fall for the biggest myth when doing self-learning. They think that the longer the course, the better it must be. So they pick the 50-hour one over the 10-hour one because more hours feels like more value, but it's actually the opposite. A 50-hour course isn't a better deal. It's just a longer road to the same place packed with stuff that you'll never even use on the job. So you hit play on lesson 1 and go through the course, but nothing's really sticking and everything feels like noise. You don't know why you're learning any of it because there's no problem in front of you this stuff actually solves. So none of it sticks. You watch the same lesson 3 times and it still doesn't click. And eventually you'll do what everyone else does and they'll drop it. Not because you're not smart enough to learn Python, but because you were set up to fail from the very first click. You can pour weeks into a course like that and feel completely overwhelmed, have nothing to show for it. No project, no proof, just half-finished progress bars and a quiet voice telling you that maybe Python programming is not for you. But that voice is lying. And the course was the problem. Not you. So if that's the wrong way, what did I actually do? Let me take you back to the very first project at my very first data analyst job. So when I landed my first analyst job, I didn't know Python at all—not a single line. I didn't need it to get hired, and most people don't either. You can land a data analyst job today without any Python, but more on that later. On the job, a problem showed up that only Python could solve, and that problem became the whole reason that I learned it in the first place. My very first project was building a dashboard. It pulled from a stack of Excel files that got dropped in a folder every single month, and before I could. Even use them, I had to clean them up by hand. This was HR data coming from an old legacy system, so every month I'd go in, delete the people who are no longer at the company, fix the messy spots, and stack all the files together and load it into Tableau. Then I'd manually refresh the dashboard every single month by hand. And that's when it hit me: this cannot keep being a manual job. It has to be automated. And the only real way to automate at the time was Python. So instead of saying, "Oh, let me go learn Python." Which was what I tried to do before and failed at. I did something different. I picked a North Star, and my North Star wasn't let me just go learn Python. It was more specific than that. It was I need a Python script that reaches into this folder, pull these Excel files every month, and runs the exact same cleaning steps on each one, and stacks them together, and updates the dashboard on its own. That one sentence changed everything because now I wasn't trying to learn a language. I was trying to solve a problem, and those are two completely different games. Now, once I had that North Star locked in, the next move is where the real shortcut lives. Now, with that goal in front of me, I worked backwards. I looked at what my script actually had to do, and then I let that decide what I needed to learn. Nothing else. I needed to read files from a folder, so I learned how to do that. I needed to loop through each file and run the same steps, so I learned loops. I needed to work with dates because I was filtering people by their dates, so I learned a bit of datetime handling. And I needed the basics that everything else sits on. So strings, lists, all that simple stuff. That's pretty much it. Now think of everything I skipped. All those advanced topics that were eating people alive in the courses. I didn't touch any of that. I didn't learn classes. I didn't learn generators. I didn't learn 75% of the curriculum because my project didn't ask for it. The honest truth is I only needed loops and a little bit beyond. A fraction of what those giant courses throw at you. And because I had that North Star, I could be ruthless about it. Every time a lesson came up, I asked one question. Does this get me closer to my script? If yes, I learned it. If no, I skipped it without guilt and just kept moving. That is the unfair part. While everyone else is trying to swallow the whole language, I was just eating one specific slice. They were learning Python in case they need it someday. I was learning Python because I needed it right now for something real with a deadline. You learn 10 times faster when the thing that you're learning has a job to do today. But learning just enough to limp through isn't the same as getting good. So here's the second half of how I locked in. As I was picking up each piece, strings, loops, lists, I didn't just read about them and move on. I take one piece and use it in a tiny challenge. Something small and fun that forced me to actually write the code instead of just watching someone else write it. So I built silly little things like a simple board game in Python just to drill loops and lists until they felt natural. Small reps, real code every day. Now here's what most people will skip, and it's exactly why they forget everything that they learn in a course. Watching a lesson feels like progress, but it's not. Your brain doesn't keep what it doesn't use. So every new piece that I picked up, I put to work immediately. First in a small challenge to make it stick, and then in my actual project to make it count. The challenge built the muscle and the project gave it a reason to exist. And that combination is what made it click very fast. So let me put all of this together into something that you can copy step by step. All right, step 1, pick a real North Star, not just learn Python. A specific thing that you want to build or automate, and the more specific, the better. Clean this messy CSV and spit out a summary beats get good at pandas every single time. Step 2, map out the steps. Write out in plain English what your project actually has to do from start to finish. Do not write any code yet. Just list the moves. Read the file, clean these columns, combine everything, save the result. Step 3, reverse engineer the skills. Look at each step and ask what Python you'd actually need to pull it off. That short list and only that list is what you learn next. Everything else can wait. Step 4, learn it just in time, not just in case. Go find the lessons for those exact pieces, learn them, and use them right away while they're fresh. You're not trying to finish a course. You're trying to finish a project. Step 5, drill each piece with a tiny challenge, then drop it straight into your real project. Build the muscle, then put in the work. That loop is a whole thing. Do that and you'll have something that most course takers never get, a finished working project that you built with your own hands. Now you might be thinking, hey, that's great for you, Kedeisha, but you had a job that handed you a project. I don't have a job yet. That's fair, but you don't need a job to get that North Star. You just need a problem and problems are everywhere. You can grab a free dataset on something that you actually care about, ask real questions off of it, or even automate something annoying in your real life. Like renaming a pile of files, automating and tracking your monthly budget, for example. You can take the messy export that your current job already gives you and clean it up in a script. Any of that works. The whole trick is to stop learning in a vacuum and start learning towards something. Because the second you have a target, the right Python to learn becomes obvious. And don't worry about the gaps. You'll always have things that you haven't learned yet. Everyone does, including the people who finish the whole course. The difference is that your gaps get filled by real projects as you need them, which means they actually stick and you can actually learn things faster. Other people's gaps will stay open because they learn 100 things that they never use and forgot 99 of them. Depth doesn't come from consuming more, it comes from building more. All right, so let's bring this home. I'm going to leave you with a simple version of this. Most people fail at Python because they try to learn the whole language before they solve a single problem. The unfair way is the opposite. You pick one real thing that you want to build, you work towards that small thing, you work backwards to find the small slice of Python that it needs, and you learn that slice just in time, and you drill it with quick challenges until it sticks, and you skip the rest without guilt. That's how I went from a half-finished course with a voice in my head saying coding wasn't for me to a working automation script that ran my whole monthly report for me in a fraction of the time that it takes to finish a course. So this week, don't open another course. Open a blank file, pick your North Star, and write down the steps. That one move puts you ahead of almost everyone else grinding through a 40-hour course that they'll never finish. In the next 12 months, one of two things is going to happen to your career. Either you're going to learn how to leverage data analytics and AI to become irreplaceable, or you are going to watch someone less qualified and talented than you take the opportunities that you deserve. Because right now, there is still a gold rush happening in data, but the rules of the gold rush has completely changed. But most people are trying to break in by playing by the old rules. So let me show you exactly what going all in on data analytics and AI actually looks like this year, because it's not what you think. If you're new here, I'm Kedisha. I am the founder of Data in Motion, a community of over 80,000 current and aspiring data professionals. And I've helped thousands of other career changers break into data analytics without going back to school, without CS degrees, and landing roles at Fortune 500 companies. I have watched this field transform in real time. And what I'm seeing right now is one of the biggest shifts that I have seen since I broke into the field from a delivery driver. To 6-figure jobs. Okay, so the gold rush is still on, right? That's still real. Companies are still drowning in data, desperate for people who can make sense of it. But here is what most people don't realize. The bar for actually getting in and getting hired has gone up significantly. Years ago, you can just learn any SQL tool, any BI tool, and build a portfolio, and you can just get hired pretty quickly. That path still works, but it takes way more now. Meta is laying off 8,000 people, not because they're struggling, obviously they're not, but because they're replacing one type of worker with another type. And they're not the only ones. Companies across every industry are restructuring their teams around these higher standards. Okay, so what does this mean for you? It still means that companies are still hiring for analyst roles. It's just that they're not looking for what most people are thinking that they're looking for. They are looking for that so-what person, the person who can walk into a room, present an analysis, and make a recommendation that actually changes how the business operates. That is the new standard. Everything else I'm about to tell you is built around that. The second major shift is AI, and this is one that's going to surprise you because the danger isn't that AI is going to be taking your job. Everything you hear about that is a lie. The danger is what it's doing to the people trying to get into this field. What I'm seeing right now from aspiring analysts is very alarming. People are using ChatGPT to write their entire queries, building their entire dashboards, and generating their entire analyses, and then putting all of that in their portfolio and in their reports. And that's what they think leveraging AI is all about, but it's not. That's outsourcing your brand and your strategic thinking, and that is creating a massive gap between people who can actually think And people who just know how to prompt. Companies are actually catching on to this. I'm going to show you exactly how they're testing for this later on in the video. But just know this: the people who are winning right now are not those who use AI the most. They're the ones who know when to stop using it. And here's the part that should actually excite you if you're coming from a non-technical background. This field is designed for career changers, maybe more now than ever. Because if companies are hiring for judgment, business context, and the ability to communicate insights to non-technical people, your industry knowledge combined with data skills—that is a combination that companies are struggling to find. And AI cannot replace. All right, so let's start getting specific. What exactly do you need to focus on to capitalize on this shift? Because most of the advice that you're hearing online is either outdated, flat out wrong, or bought and paid for by some sponsor. And I'm not just going to tell you, I'm going to show you. All right, so first off, independent problem solving comes first before any tool. Before you touch any software, you need to understand how to break down a problem. What is the actual question? How would you measure the process? What What data do you need and what would a good answer look like? I like to use a framework called DMAIC— Define, Measure, Analyze, Improve, Control. It comes from Six Sigma. It's simple because it forces you to define the problem before you even open a dataset. Most people skip this entirely. They just jump into exploring data with no question in mind. That is not analysis. That is just scrolling through columns. All right, so let's walk through a DMAIC problem step by step using an example of being a Data analyst at a hospital. So let's just say for step 1, for our define, we see that the problem is 30-day readmissions jumped from 12% to 18%. So why the heck did it jump 4 points? So what we're gonna do is first we're going to have to really measure this process of 30-day readmissions, but we need to pick measures all about picking the metrics, right? So what is the readmit rate, time to readmit? Whenever you're doing averages or medians. Statistics, this is where it comes in, your measure. Then you're going to analyze. So after you know the metrics that you're going to use and analyze for, then you're going to create charts. And this is an example. So here, if you create a chart and you can clearly see that the top diagnosis that is contributing to the high readmissions rate. So one thing that we can see with this Pareto chart, there's practically the top 1 through 4, or just a very few amount that's contributing to 80% of the readmissions rate. And so something like that, you take and you go to the improve stage and improve is all about providing the recommendations. So anytime you're doing your courses, your capstones, wherever, and the prompt is, hey, provide recommendations. It is all about how do you take what you found out in the analyze phase in your chart? And provide recommendations to always improve process. Remember, we're always just analyzing business processes. Everything can go boil down to a business process. And whenever you provide recommendations, it's all about improving something, whether it's reducing risk or improving revenue, whatever it is. And then control. And so this is where the control step is where your dashboards come in. So again, as analysts, we are analyzing business processes and dashboards. Visualize business processes. So again, dashboards are not just pretty charts, but this is where the decision makers can pretty much see whether a process is normal, whether it is out of bounds or abnormal, where there's outliers, whatever it is. But this gives them the visual view so that they can see this and then they can take this and go to the next steps. Jeff Bezos had a great example For this, during the days of Amazon, their metrics showed that customer service wait times were under 60 seconds, but customers were complaining nonstop that the wait times were too long. His team defended the metric, so Bezos picked up the phone and called the 1-800 himself, and they waited in silence for over 10 minutes.

So one day we're in a meeting and we get to this metric in the deck. The guy who leads customer service is defending the metric, and I said, okay, let's call. And I dialed the 1-800 number and called customer service. And we just waited in silence more than 10 minutes.

That is when the metrics but the anecdotal data was not lining up. That is what an analyst does that AI cannot. Being able to question the metric, not only creating metrics, but lining it up with the anecdotal data. Whenever they don't match, investigating why. Okay, next up is a core technical stack that is non-negotiable. So Excel for pivot tables, Power Query, descriptive statistics, SQL for joins, CTEs, window functions, aggregations. This is your technical foundation that is still getting tested in interviews. But here's where most people are falling short. Data storytelling. And I don't just mean making charts. I mean actually knowing the difference between a good visualization and a bad one. Let me show you. So let's take a look at the difference between what a great dashboard looks like and what a not so great dashboard looks like. So when we first start learning Tableau and Power BI, all of our work looks like this. Now the issue is most of us stay here and not actually know what great looks like. And we put these types of dashboards on our portfolios. Uh, we move on to SQL and Python with a skillset such as this, and we don't really improve our storytelling and dashboard building skills. And this is where most of us mess up. But what we ultimately want to do is graduate into good looking clean dashboards such as this. So a dashboard like this, this is going to be the next level that you should be aspiring to and getting to before you're applying for jobs. If our dashboards are still looking like this, we should not be moving on to another tool until it looks like the previous one. Most people are building dashboards that look like the first one, and that's not storytelling. It's just plain noise. Nobody is making a business decision based off of that. You need to be able to also build consulting-style slide decks. This is a completely separate skill from just building dashboards, and it also gets tested through take-home assessments, which are becoming more common, especially for higher-paying roles. They'll give you a dataset, a business problem, and a deadline. You build the analysis, the deck, and present it. If your deck looks like a data dump, you're not moving forward. The tool gets you in the room, but the thinking is what gets you hired. All right, so let's get real about AI again, because everyone is telling you to use AI as your pilot, which is completely wrong. They are overstating how effective it's becoming. And they're making it sound like you can just dump a dataset into ChatGPT and allow it to do the job for you. It won't. AI should always be your co-pilot. You are always the pilot in charge. And that distinction matters more than anything else I'm about to say in the rest of the video. Use AI for the mechanical grunt work. Data cleaning, debugging, formatting, brainstorming. All of that makes you faster and all of that is fair game. But here's where you stop. Do not use AI to write your analysis. Do not use it to generate your recommendation, and do not just paste a dataset into ChatGPT and say, ask for insights. And this isn't just my opinion. MIT even published a study that showed AI boosts your output at first, but it weakens your ability to solve problems on your own. The people who relied on it for direct answers got worse over time, and the ones who used it more like a hint system where they still had to do the thinking stayed sharp. So here's the rule. AI does the labor, you do the logic. All right. So this is the last one and what also brings everything together. Every project on your portfolio should start with a business question and end with a recommendation. And a great way to do this and apply everything to a real business problem is to find a local nonprofit and offer 30 days of free analyst consulting. Clean their data, build a dashboard, and deliver a consulting style deck with recommendations. This becomes part of your portfolio, your case study on your resume, and real experience that you can talk about in interviews. And this is what separates you from everyone else applying for the same role. I have said this in multiple videos and I will continue saying it over and over again because still not enough people are actually doing it. So if you start doing this today, I guarantee you, you're going to shoot up to the 1% of the competition. All right. So I just threw a whole lot at you. So let's break this back down. The gold rush is still going on, but the bar is just higher. Problem solving before tools, storytelling that drives decision, AI as your copilot, real business projects, not just tutorials. I've reviewed over 500 data analyst portfolios in the past few years, and here's the truth. Most of them are copy and paste twins. The Titanic, COVID dashboards, Netflix charts, and then people wonder why they're not getting callbacks. If your projects came from the same step-by-step tutorial, you don't have a portfolio, you only have homework, and hiring managers can tell. The good news, the portfolios that did land interviews shared 3 traits. I call it the portfolio blueprint, and today I'm gonna show you the mistakes, the fixes, and the real-world examples that you can model. So if you want some interviews, stay with me. Okay, let's start with the first biggest mistake. Your portfolio looks like everyone else's because you followed the same tutorial. I've seen the same Spotify dashboard at least 40 times. The same step-by-step walkthrough where someone just follows along a YouTube video and puts the end result on their portfolio like it's original work. Now here's a problem. Hiring managers see these too. They know exactly what a tutorial project looks like. And when they see one, the message they receive is, this person can follow instructions. But that's not what gets you hired. What gets you hired is this person can think. Copy and paste projects only tell a hiring manager that you're a follower. Original projects tell them that you're a problem solver. And in this market, problem solvers win every single time. So what should you do instead? Build projects around real business problems in real industries. So step 1, pick your target industry, healthcare, finance, marketing, supply chain, whatever you're aiming for or coming from. Next step, research the actual problems in that space. You can go to Reddit, read industry blogs, use Perplexity or ChatGPT and ask, what are the top 5 challenges facing healthcare operations right now? And then step 3, find a real dataset that connects to one of these problems. Not another Kaggle popularity contest, but a dataset that represents a real scenario a business would actually face. And then build 2 deliverables. First, a dashboard that answers the core business question. And second, a consulting style slide deck that presents your findings and recommendations. This slide deck is critical. Lead with the answer first. The paraben principle. Every slide should have an action title at the top, not just sales by region. That just tells me nothing. Try West Coast sales dropped by 15% due to supply chain delays. That is an action title, and that's what gets a hiring manager to lean in. A recent survey of hiring managers found that 84% are willing to hire a candidate without a relevant degree if they have a portfolio demonstrating the exact technical skills required for the job. So your portfolio can literally replace a degree, but only if it shows you solving real problems, not just copying tutorials. And don't just take my word for it. I want to show you some projects and portfolios from my students so you can see it in practice. So pulling this one up, looking at the projects, not a single generic dataset in sight. Every project is tied to a specific industry with a specific business problem. This person isn't showing you that they can make charts. They're showing you that they can think like a business analyst. That's the difference. Okay, so mistake number 2, and this one is a silent killer of portfolios. Here's what happens. You click on someone's portfolio and all you can see is a dashboard, maybe some charts, maybe a link to a GitHub repo full of raw code, and that's it. No context, no explanation of why the project exists, no storytelling, no description of what they found, and no recommendations. Just output. If someone just walked up to you on the street and handed you a chart with no context, you'd say, Okay, what am I looking at? What should I care? Or why are you walking up to me in the first place? That's what hiring managers see or think when they are looking at your portfolio without any narrative. Data without a story is just noise, and no one hires noise. A hiring manager isn't just looking at your portfolio to see how you can make a bar chart. They already assume you can do that. They are looking to see how you can think. Can you define a business question? Can you explain what the data told you? And can you translate numbers into a decision? That's the job. The tools are just a vehicle. Every single project in your portfolio should walk the reader through 4 things. First, the problem. What business question are you answering? The VP of marketing wants to know why customer acquisition costs increased 23% last quarter. Second, your approach. What tools did you use? What assumptions did you make? This is where you show your process and not just your result. Third, your key findings. Not every finding, just the key ones. 2 to 3 insights that actually matter. Lead with the insight, not with the chart. And fourth, your recommendations. This is where 95% of portfolios are missing entirely. What should the business do with this information and what's the next step? If you can't answer that, you're just presenting data. You're not doing analysis. Okay, let me show you what this looks like when someone does it right. Look at how they set up each project. Before you even see the chart, you understand the problem, you understand the data that they use, and you understand what the stakes are. And by the time you get to the dashboard, you're already invested in the answer. That's the difference between a portfolio someone glances at and a portfolio someone actually reads. Context first, visuals second, recommendations last. Every single time. Quick pause. Now, if you want to land your first job in data analytics and avoid the lengthy mistakes that I did, click the link in the pinned comment where you can find the exact steps that my students use to land their job in data analytics without going back to school. All right, now let's get back to it. Okay, mistake number 3, and this is the one people underestimate the most. You can have solid projects with great storytelling and still not get a single callback. Why is that? Because nobody can even find your work, or when they do, they can't figure it out. I reviewed portfolios with broken links, portfolios hosted on GitHub with no README, 15 randomly named repos, portfolios on Notion pages that look like someone's personal junk drawer, portfolios with typos in the headline. Let me be direct. A hiring manager is not going to spend 10 minutes digging through your GitHub repo to try to figure out what you built. They're just going to click see chaos and close the tab. That's it. And then you lost them. Your GitHub is not your portfolio. It can support it, but it's not the thing you should be sending to recruiters. So here's a filter. Every portfolio needs to pass what I call the 3 E's test. If it doesn't pass all 3, it's not ready to be shared with anyone. E number 1, easy to access. Can someone actually get to your portfolio in one click? If you're sending people a Google Drive link that requires a permission or a repo that they have to clone. Or a zip file, you've already lost the game. Your portfolio needs to be one click away. A Notion page, a Canva site, a simple website. A recruiter should be able to open it in 3 seconds from their phone. E number 2, easy to navigate. When someone lands on your portfolio, can they immediately see what you've built? Or do they have to scroll through walls of text, hunt for project links, or decode your file structure? The best portfolios have a clean layout, your name, A one-line description of who you are, 3 to 4 project cards with clear titles. Click a card, see the project, no treasure hunt. E number 3, easy to consume. Can a hiring manager understand your project in 2 minutes or less? Because that's about how much time you're going to get. If they have to read a 10-page write-up or scroll through a 30-tab workbook, they won't. Lead with the problem and show the key insight and give a clear recommendation. If it takes longer than 2 minutes to get it, simplify. And here's a strategic layer that ties it altogether. Your portfolio doesn't live in a vacuum. It connects your LinkedIn and your resume. I call this the brand trifecta. Your resume communicates impact, your LinkedIn will build visibility, and your portfolio proves you can do the work. When all 3 are aligned and telling the same story, you become very hard to ignore. And curate ruthlessly. You do not need 10 projects. 3 to 4 polished ones that all tell the same story. I solve business problems with data, and here's the proof. And when your portfolio is positioned around specific industry or problem space, you're not competing against every other analyst on the internet. You're creating your own lane. That's the blue ocean strategy. Stop fighting in the red ocean where everyone else has the same generic projects. Niche down, specialize, and let your portfolio do all the selling. This is what happened with my student Caitlin. She was a teacher, no data background, but she built a clean portfolio, had well-crafted projects, and optimized her LinkedIn to match. And posted it publicly. A recruiter saw it, and within a week she got an offer as a finance analyst. She built proof and packaged it professionally and put it where people could find it. That is the game that wins in the new job market era. So that is the portfolio blueprint. Solve a real problem, show your thinking, and package it like you mean it. You don't get paid for what you know, you get paid for what you can prove, and your portfolio is that proof. So stop treating it like homework and start treating it like your ticket out. We are going to be building a $100K data analyst portfolio from scratch. I'm going to show you the best way to source really good projects that are not from Kaggle. What I'm going to show you how to do is also do all of this with AI. I'm going to be using Manus because I love Manus. Manus is my favorite AI tool. You can do this with any AI tool. You can do this with Claude. You can do this with ChatGPT. But we're going to be using Manus to be sourcing, finding projects. Of course, our datasets and also helping us do some preliminary work as well. All right, so when it comes down to our portfolio, it's all about having high-value projects. And high-value projects are all about pretty much projects that cover business problems and things like that over, you know, high-value business problems per se. So for example, retention. When it comes down to business, there's, you know, 3 Rs, 3 main Rs: revenue, retention, and risk. Pretty much every business problem or any person in a business, your work is supposed to be tied to any of those 3, whether it's going to be increasing revenue, reducing risk, and increasing retention. That could be both employee retention, also customer retention, right? And all of those all relate to the bottom line when it comes down to revenue, whether you're doing a nonprofit, working at a nonprofit, or even a for-profit a company, it's all about staying in business. And to stay in business, you have to be profitable and you have to make revenue. So you want to build your projects around, you know, those things. And a great way to do it, especially when you're a career changer or a way to step away or step above like Kaggle. One thing about Kaggle, like I used to really like it when I was first getting into data, you know, 5+ years ago. But the thing about Kaggle that is a knock is that there's so many people, like anyone can like upload a dataset in Kaggle. The quality could just be a lot of like really fake randomized datasets. That's like not really worth your time. All right, so when you're trying to get your first analyst job, there's 2 types of projects that you will always be working on: projects for practicing and then projects for showcasing. I would say, well, 80% of your time you'll be doing projects for practicing. For, you know, when you're learning Tableau, learning SQL, where you're learning these new skills or tools, you're going to be doing, you know, projects and challenges, things like that to develop your skills. But projects for showcasing, that's completely different. That's just like, you know, the difference between getting dressed to go to Walmart versus like getting dressed to go to like your prom, right? You go to your prom, if you did go to prom, like, that's like you get dressed up very well, very fancy, spend a lot of time getting ready. That's like once in a while or whatever. That's just an occasion. But you know, you get dressed to go to like, you know, Walmart or whatever, whatever it is, like often. So it's a different level of preparation basically and time commitment. A lot of people will put these very projects that they did for practice that's not very polished on their portfolios, and then they wonder why they're not getting like callbacks, and then why they're not getting like the reaction that they would like. When you do projects for like practicing and developing your skills, you don't really put in the effort to make sure they're super polished, making sure the storytelling is like, you know, up to par, whatever. I want to show you the differences between the two. We are going to be doing projects for showcasing on our portfolios. And the idea about your portfolios is that these are going to be 3 really high-polished projects that you spend a good amount of time on them once, and then they compound as far as like do work for you like afterwards. Like you don't have to put in the same amount of effort every single time. Again, you could use ChatGPT, you can use Claude, Perplexity, you can use Gemini. We're going to use Manus today, which is another AI tool, and we are going to ask Manus some Basically fish us a high-value project and also the dataset. So I'm going to say, I am an aspiring data analyst. I want to build a portfolio project on a high-value problem in my target domain, being supply chain. Can you find me real datasets online and build a CSV on— I don't know, that may be ACES, but build some CSV files or file, you know, for my data analysis project that I can do in, you know, Excel or Tableau. So the great thing with Manus is that it has like inherent agentic abilities in there. So what I'm going to do is press send and just see what happens. Asking me, hey, focus areas, you know, supply chain is broad. Do I have a preference or should I pick the highest impact one? Right. That's a good question. Inventory management, demand forecasting, supplier performance, logistics analysis. So what I'll say is I want descriptive analysis. But you can decide the rest. And again, you could always say whatever you want, but those are really good. So that's another thing with Kaggle. Sometimes there's a lot of datasets that's really designed for like data science predictive stuff that's not very good for, you know, descriptive stuff. So that's a very good question. Avoid Kaggle, right? That's one thing I love about Manus. You can QA the AI in real time because it's saying exactly where it's finding the sources and things like that. So it says it's found a dataset from the Brunel University and then something from data.gov. So those are real good. Anything with data.gov is a good chance it's going to be some, you know, data cleaning. So I wanted to do something that's not Kaggle. It found some stuff for us and it's going to be nice to see what this looks like when it's done. No, do not clean it. Do not clean These. So I did say, so again, when it comes down to QAing the AI, it was saying that it was going to clean it up because I had said that, hey, I want to do some analysis in Excel and Tableau. So it took it upon itself to say, hey, let me go above and beyond and clean it. And I say, nope, nope, nope, don't clean it. We want this run authentic so that, you know, that's stuff that we could do, you know, for our project. And then they even gave us a project brief, which is pretty cool. So it gives us a project brief and then it gives us a zip file of all of our data. So we have 1, 2, 3, 4, 5, 6, 7 datasets. So look at that. So this is even stuff that honestly you can also take into SQL and do some joins and stuff. Again, way better than just doing some stuff on like, say, like Kaggle or whatever. Okay, so I'm going to make a new folder and say, you know, supply chain. All right, so let's select folder, create the folder, and then extract. And so cool thing is that it even made a project brief in Markdown. I'm going to go back to Manus and tell it to export the project brief in Word doc. One thing that I love about Manus is that You can QA, quality assurance, the AI in real time. You see the steps and say what it's doing. The project brief being in a markdown file, so it's asked it to export in a Word doc so I can just look at it easier. We'll see what this project brief looks like. You know, it's pretty much on you if you'd want there to be a project brief. If you want to look through the data first, it should be exporting the brief. Let's look at the project brief. So we have 1, 2, 3, 4, 7 datasets that's interconnected. It gives us some suggestive descriptive analysis angles. So even before looking at it, it gives us some suggestions and angles that we can look at. So I haven't looked at it yet. We can look at order fulfillment for on-time delivery, transit time. It's giving us questions that we can do, capacity, warehouse utilization, things like that. So this is really good. So again, this is a dataset looks like they got from a university. And let's actually just look at what we have so far. So we've got freight rates. Okay, so we have stuff like this. We have order lists. We have, say, order IDs, unit quantities, whether it was shipped late or not, costs. So again, for the sake of like an actual project, like this is really good stuff. Like it's multiple CSV files. We can use AI to help give us an idea of what's going on, an idea of just getting started. But again, we can look at through all of these CSV files and just do some good analysis for a project. So this is good stuff. This is way better than something that you find on Kaggle. Again, we can do this. You can literally do the same sort of prompt for whatever high, you know, domain you want to do. If you're not sure which domain you want to do, yeah, like you can kind of take the MBA approach, right? And MBA, for your MBA, all you have to learn about all areas of like general business, right? Finance, marketing, accounting, strategy. So you can kind of take that approach and then do things like, you know, do something like into product, do a prompt to find a dataset relative to marketing, do a prompt to find a dataset relative to healthcare and And again, I love Manus. I recommend Manus because it is, it has some agentic features in there where you can actually QA the AI in real time. As you can see, there was times where I would see that, hey, it's cleaning something or it's doing Kaggle, find me something with Kaggle. And I'm like, you know, you can message it and talk to it real time, be like, hey, nope, don't do that. You can divert something else. So for the sake of the video, what I'm going to do actually, there will be a link in the description. To this dataset and also the project brief, along with some other ones I'm going to make as well. It's going to be like a package of like a, you know, portfolio challenge where you guys can, you know, do these high-value projects that are, you know, with real data that's sourced not from Kaggle. Let me know in the chat if you'd like a, you know, Mantis for data analytics video tutorial. If so, like I said, I love using Mantis. I use Mantis all the time. And I do think that, you know, the AI-powered data analyst, AI-powered full-stack data analyst is the new thing that everyone has to do. So let me know in the chat if you want a Madness for Data Analytics, AI for Data Analytics, like tutorial series. We'll go from there. There's tons of resources on how to do these tutorial projects, like step by step, like, hey, watch me do this project end to end. There's a bunch of end-to-end projects that people can follow. How many people have taken courses? Everyone raises their hand. But how many people have, you know, done projects? Everyone raises their hand. But how many people have a portfolio? Very few people have an actual portfolio. And the thing is, when it comes down to landing a data analyst job today and in the future, especially in this new AI job era, you have to think of a portfolio just like your resume. Like, everyone has a resume, or they know that they should have a resume. You should think of the portfolio as like top of the line as your resume. Like, everyone should have a great LinkedIn, everyone should have a resume, and everyone should have a portfolio in this job market. All right, so for the sake of this video, we're gonna do is I'm actually going to build out the actual portfolio with completed projects that my students have made based off of things that— projects that I designed for my program. But what we're going to do is we're going to first pick out a portfolio website. So I love Canvas. I mean, Canvas. I love Canva. They have tons of portfolio websites. So if you go to templates and then let's say we go to Website and portfolio. Website portfolio is a good one. Again, there are tons and tons of, you know, templates that you can use and it's very, they're very easy to edit, manipulate. There's tons of them, right? And it's easy to just go in and edit. So what we're going to do is we're going to use a template that I already designed. I actually combined a couple of them into one. It's like a combination of this one and some other ones. So we're going to start with that template. Let's see this portfolio template. And I'm going to also have this template. So this is a link of it. I'm also going to have the link available. This is a template. Let's see, view template and an editor that's opened as me. So what I'm going to do is I'm going to make a copy of it. All right. Then we're going to call this a Dem Portfolio Template, Dem One Pager Template. YouTube. Okay. Now I'm going to show you how easy it is to edit a portfolio template on Canva versus say like another website. Even if you vibe code, I would not vibe code, do any of those vibe coding sites for them. Again, when it comes down to portfolios, there's really 4 E's. You want it easy to access for people to look at, easy to navigate. One-pager portfolios do that. Easy to consume, right? One-pager portfolios do that. And for you, you want it easy to maintain. So we're going to show like how easy it is to just copy and paste and add these text boxes versus, you know, working with code. This is an actual, my actual portfolio site that's not as updated, but this has a combination of like about me has a combination of like a bit of my resume at the time. And then some actual projects that I did in the past. The thing about portfolio projects, you know, we're actually putting in projects that you actually spent time on. Honestly, this is something that I did, this email marketing project. I did this project in 2021. This marketing plan project I did in 2020. Yeah, 2023. And I can still, if you ask me about these projects, I can still walk you through them like I did them yesterday because I actually did them and I spent like way more time on these projects, building the deck, then like the projects for practicing that I mentioned, right? Because you want to put in an extra amount to make it polished and things like that. So one of the reasons why I mentioned this is because when I was learning and doing these like projects, when I was taking courses, I had like, oh, hey, these done for you kind of like tutorial projects that you could just do step by step. And then just like, you know, hey, just put them on your portfolio. I would do these projects. You know, do a step-by-step tutorial projects. And then the next day, I wouldn't even know what the heck I just did. Right. So I felt like a fraud having these projects on my portfolio that I couldn't even talk to you about it. So that's why you want to avoid doing that. Let's remake this right now. And let me pull up the portfolio projects that we're going to actually use for the project. So let's see, this is a project. Okay. So we're going to have something. So this is a milestone project in my program that one of my students did. So this was done in Excel. So we're going to do something with Excel. We're also going to have a— we're also going to have this. This is a project for our internship. We're also going to have a Tableau dashboard. And we're also going to include— we're also going to be including an executive consulting slide deck, right? So we're going to have like those like 3 versions in our portfolio design, in our portfolio. All right. So you want to have a picture, right? So I'm going to keep my picture there. You want to have an about me. So again, this, this very first, when you're designing your portfolio, you definitely want to have a picture, definitely have your name. And then you want to have links. Consider this like your first impression, just like on your LinkedIn, right? On your LinkedIn page. As soon as people open to your LinkedIn page, you have your banner, your, your Your photo, your headline. For your portfolio, same thing. And your About Me is basically like your elevator pitch. And then like your list of skills. But one thing that you can do with Canva, it's easy to create text boxes for links. So like, say for example, let's say I wanted to create a text box to create, I don't know, you know, to a Tableau Public page or something, right? So you do Tableau Public. That's a great thing that I love about Just create a text box, you know, Tableau Public, Tableau Portfolio, for example. And then what you can do is you can double-click and then click this link. And then you can just literally paste in the link to your Tableau Public profile. Let me see if I can find my old Tableau Public profile. Author. And here I am. This is the very first Tableau dashboard that I did. As you can see, this was first published September 18th, 2021. This is the very first public one, I would say. I think the very, very, very first one that I had, I must have deleted it because I thought it was ugly. You can kind of see the progression over time. So that's the great thing about Tableau. You can just kind of go back and see. Let's say, for example, I'm just going to copy or duplicate this and I can do a duplicate of And say like Tableau Public, Tableau Public Portfolio. And when I double-click, I click the edit link and I just paste in the, and then, yeah. And so my LinkedIn profile, you definitely want to have a link for your email, your resume. And for your resume, what you want to do is you want to upload your resume to a Google Drive. And then you want to make sure that Google Drive link upload is like accessible to everybody. And then here you just paste in the link to that Google Drive of your resume. And then same thing for your LinkedIn profile. You want to have a link for someone to email you directly, PDF of your resume, a link to your LinkedIn. And you don't have to have the Tableau Public. You can just have it there if you want, but you don't have to have it. Just did that there so y'all, y'all can see how easy it is to use this template and create an actual, you know, the links. All right. But as you can see, no code so far. So when it comes down to that, or so that's that there on the right side, again, your about me and then your skills. Your about me is basically your elevator pitch. Everyone should have an elevator pitch, right? You should always be ready whenever you're ready for applying for roles, right? If you think about it, you build your skills, you build your brand, then you build your story. You work on crafting your story and then create your experience. Once you're applying for roles or whatever, you never know when You know, an opportunity can come. You don't know who you're speaking to and you always want to present yourself well online. So your about me, it's literally like just a few sentences of like a version of your elevator pitch, right? So this is an old one that I have, but I'm just going to keep it there. For your skills, the way I like to design, you know, have this set up is like left side. I have like left side for like, say like harder skills. And then we'll have like your right side for like your soft skills. So left side, I have times like SQL, data viz, data storytelling. I don't know why I have data viz twice. I can do Python, Tableau, you know, machine learning. On the right side, you know, very strong soft skills, not just soft skills, but strong soft skills. So problem solving, creative thinking, Six Sigma, something that I have a green belt in Six Sigma, process improvement, leadership, community building, right? Not team player. Not great listener. Okay. None of that stuff. Actual strong soft skills. And when it comes down to today, problem solving, creative thinking, leadership. If you have those things, you better highlight them and put them like top 3. Those are things that folks really want. And those are things that are degrading by the second with AI today. Okay. So this next section, this is a, this section is optional. This is a section that where if you want to kind of like Intertwine your resume in here. So this is quite old. I already graduated, but just going to put that there for now. You know, you could have your degrees here if you have certifications. And then just like your, literally like your resume here, but just kind of like brief. And then it goes on to the projects. So let's redo these actually. What I'm going to do is I'm going to just make kind of like new ones fresh. So what I'm going to do is I'm actually going to delete And we're going to make some new pages. We're going to make some new pages. We're going to add a page. We're just going to delete and kind of start fresh here. So let's start with our first project that we're going to use. We're just going to use this Excel project. All right. So this has, you know, this person has some visuals here. This project was about analytics about college majors, like graduation for, you know, employment, unemployment. Stuff like that. Also salaries. So what we're going to do is we're just going to take some of these, maybe like the strongest, what we find is like the best visuals that we would like and add it to our, and show how to like, you know, add it to convert, you know, these into a portfolio. So as you can see, the project that we had, it had over like, it had like 13 questions. We're not going to put like 13 visuals. We just want to pick like the strongest ones. So like for this, this is, this is interesting. So it's a stacked bar chart. So let's say we can just pick, I'm just going to take a recommendations. I'm going to start with a text box. That's like A kind of like almost like an action title, well sort of that kind of describes the project. We're going to do you know college college major employment and earnings analysis for I'm just going to do analysis and let's see keep it bold. All right, so this is a question that pretty much compared if there was going to be a relationship between like the popularity of a major and in. Employment rates. And by this picture, we could say no. This was one where, you know, the most popular majors were psychology, communications, biology, and then the graduates with full-time employment, you know, not as high. And this is pretty important, especially now when it comes down to if anyone's seen there's colleges around the country in the U.S. that are actually removing majors. You know, a lot of the liberal arts majors that are not You know, really panning out for like employment, employability, and earnings for people, you know, since graduates. All right, so this is Excel. So what I'm going to do is I'm just going to copy. I'm going to copy this, come back here, and I'm just going to paste. Simple as that. And then we have our visual here. And so we can do that college major there. And then let's see if we want to get a Mother, or some other visuals are pretty good. Interesting. Medium income from women-dominated majors, 33. That's interesting. Let's see this one too. Top 5 majors have at least, you know, 44% female graduates. Psychology has the highest percentage of female graduates. It's interesting. All right. All right. So let's see what else I would like from this project. Accounting is the most popular major. Okay, so for the sake of the design of the page, what I'm going to do is I'm going to replace it and just have like 2 kind of square photos here. And what I'm going to do is just have like a blurb of each, a small blurb. So you can do multiple things. So these, we teach our students to do action titles for the project. So, so these already have, you know, kind of descriptions or action titles for each chart, what you can do is have like, you know, title here and then have like a blurb describing like the project itself. So here I'm just going to take what maybe some of the recommendations, or just take some of these recommendations and have them in here. Move that over. All right. College major employment and earnings analysis. This project, you know, analyzed You know, it could be whatever amount of rows, whatever, you know, it could be analyze, you know, 20,000 rows of data covering, you know, college, covering, you know, employment and earnings outcomes for college majors. Recommendations include, and then here I'm just going to paste some of the recommendations. In creating internship opportunities, creating internship opportunities for field, and I can add one more just for the sake of it. I'm going to say it could include also mentorship programs. That's a way to win that I like, and mentorship programs. All right, so here basically title, small like blurb of the project. So here I also included. The recommendations of the project, and then just a couple visuals. All right, so what I'm going to do is I'm going to copy or duplicate this page, and then we're going to add our next project. All right, so that was Excel. The next one is going to be just a plain Tableau one, so a dashboard. All right, so this is going to be the dashboard. Let's see, there's 2 things you can do. You could export. The PNG, you can export the PNG with Tableau. Now, if you go to this top right, you can say download and download image. I will say for whatever reason, a lot of times when you do that, it looks weird for whatever reason. So I actually, I just recommend honestly taking a snippet of it. So my screen up above me is actually bigger with it on top of the screen above me. Copy, and then let's remove. All you do in Canva is click backspace, and then right. So like that's going to be way better. So now I literally just copied and pasted it, or took a snippet from the website on Tableau. This is employee attrition analysis. I'm going to just copy and paste what we have here. So again, a lot of the work whenever you're making your portfolio is already done. All right, just so it. Fit. So it's okay. And I think we can just make— let's make this 30 and also make this 30. Okay. All right. Employee attrition analysis, identifying key drivers of turnover. All right. And so here what you can do is I can just do a visual or a kind of overview. So with this, we have 11.9%, and you can say here, you know, analyze You know, analyze an employee. Actually, this is actually healthcare. This is actually in healthcare. This is actually, I don't know, in healthcare. If you're into like people analytics, again, this goes into the, the 3 Rs retention. And this actually covered a healthcare type of thing. So employee attrition analysis in healthcare. So like here we did college major employment. So this is like education. So just highlighting the industry or the domain. And this one is, we're going to do employee attrition analysis in healthcare. Let's see, key drivers of turnover. And I can just do that. Most of the work when it comes down to building a portfolio, most of the work is all in the project already. As long as you, it's all about just knowing what you're doing and can be able to explain and defend your project. So I'm just writing these out by hand. Some of you guys can use AI to help you create like a bit of a brief and things like that. It's fine. Just make sure that again, you can defend these projects if asked about it. Just the other day, we actually had a student who landed a job, a 6-figure job. And he actually showed his portfolio that he built in the program. Same Canva thing. And he showed it inside during the interview, like walk through the portfolio projects in front of the VP and he got the job. So when it comes down to this portfolio. Before you get the interview, before to get the interview, and you can utilize it as an asset to showcase during the interview. That right there is, that's the secret sauce. All right. So they analyzed attrition of over 1,600 employees in healthcare and found discovering, you know, it could be discovering an 11.9% attrition rate. With an average salary of $78K. I don't know, seems kind of low for healthcare. I'm just guessing. With the highest turnover, you know, department being cardiology and maternity. Right. So I can say something like that, like a little blurb. And then what I could do is I can highlight, utilize color and just highlight this attrition rate and say, make that bold and make that blue. And then also do the same thing here, average salary of $78K. How to bold these? Bold, blue. Okay. On this dashboard, we have 1, 2, we have 6 visuals. And most likely there were, there's probably going to be twice as many visuals that you did during the project. But what you can do is also paste in some other visuals that you did. For your analysis, let's say like did not make the dashboard. Just for the sake, what I'm going to do is I'm going to take what I feel like the most important ones. Yeah, the lower satisfaction equals higher attrition. That's really cool. Actually, I see what frontline roles have higher attrition. You can see like nurse has like higher attrition than admin, which is like zero. So I'm going to do, I'm going to just literally just copy and paste. I'm just literally going to copy and paste. And I'm just going to do it for another one. Lower salary, higher attrition. So just going to copy. So for these, for, for a Tableau project, highly encourage you guys to have basically on one side a photo of the dashboard. Sometimes the dashboard can, can take over the entire page if you'd like. And then some visuals that did not make the analysis, but, but also supports All right, so we got this. All right, and then we're going to do one more. This one is going to be one with a dashboard plus our slide deck. Let's see. So this is going to be the dashboard. This, this is a dashboard and project for our internship actually. So I'm going to copy this straight from Tableau again. Again, you can export as a PNG as an image, but I, for whatever reason, a lot of times The export from Tableau is weird, so I'm just going to take a snippet and paste it in here. As we can see from this one, this one actually takes a big chunk of the page. So what I'm going to do is going to have one page just for this dashboard. And what I'm going to do is I am going to let's see. This is multiple pages of the dashboard. What I want to do is I'm going to change the title. We're going to call this you know. Marketing analytics dash for marketing analytics for growth. Marketing analytics for let's see growth startup growth analyzed you know marketing data covering social media paid ads look operations. And sales to cover process improvements and, and identified gaps. So this is a gap analysis, identified gaps to help, you know, unlock 25% more revenue opportunities, stuff like that. And identify. Okay. So you have one page just for this dashboard here. And then what we're going to do is we're going to duplicate. And what we can do is I'm going to find Jordan's slide deck. So this is Jordan's slide deck here. What we're going to do is we're going to copy some of these. It depends on whatever. It's the same project, so we can do— so this covers like some of the data that we had that, um, this is a good one. I'm going to copy this and then I'm going to paste it here. And then I'm also going to pick, let's see, I'm also going to pick. So this talks about YouTube being the largest driver, 40% of book calls. So that's really good. We're just going to pick ones that show some like good, like benchmarking. So this one is good too. This one gives a good insight on like the non-cleanliness of the data, which is really good. Really depends on honestly the type of video that, you know, The the story that you want to provide. So for example, this was good. So this one this one highlights unclean data. You know, identifying that you know the data isn't that clean. So that's actually really good. Say like identified. We're going to duplicate, and then we're going to do some blurbs here. Okay, so identified that. YouTube provides the largest source of booked calls and revenue generation. And so here, almost 40%. So that's really good in here. And then again, we can do a bold. And then for this, we can do a blurb of just copy that and just do a blurb of identified a data quality concern. Identified a data quality concern of over 30% of calls. Click calls have no attribution source, which, or say, which calls for, you know, a data quality intervention, you know, data quality intervention. Okay, so that's concern over 30. And when I remove this, and I'm just gonna put this up here, or just gonna put it there. Okay, so let's look, we can actually see what we have so far. So what I'm going to do, we can do preview. And this is what the website would look like so far, which is cool. We have Excel, we have Tableau. Okay, this could be Power BI if you'd like. And then we also have a dashboard, another dashboard and slide decks. And then we see how we can easily use Canva to just copy and paste and whatnot. So what I'm going to do now is I'm going to Just show you how easy it is to publish the site. So here, what Canva does, it gives us our own website and hosts it for us, and we don't need to pay for nothing. So what I'm gonna do, I'm gonna do publish, and then I'm gonna show you how easy it is to have that. So our website is live. So this is an actual URL, one that I'm gonna have in the description where it literally shows. So this is something I can link to my resume. I can link it on my My LinkedIn, and look how easy it is. Just scroll down to just to see stuff. It passes the whole test. Easy to access URL, easy to navigate, one pager, easy to consume. It's easy to update, right? You saw that easy to maintain. It's just copy and pasting, right? So I'm going to have this linked in the description. The template is going to be in the description. The, this finished portfolio is going to be in the description, and also I'm going to have A data portfolio analyst portfolio challenge with the supply chain dataset and also a couple others that you guys can do. You probably don't want to hear this, but the one thing standing between you and that high-paying data analyst job is not another course. It's the right portfolio project. And no, not the projects from the Google Data Analytics course or some random walkthrough tutorial on YouTube. I'm talking one strategic. Strategic, real-world project that screams, I'm already doing the job. But most people skip this, and that's why they're stuck on the job market. But if you want to bypass the gatekeepers, dodge the no experience excuse, and finally land interviews that lead to 6-figure job offers in data analytics, this is the portfolio project that will do the job. And no, it doesn't require a fancy company, another degree, or a ton of What I'm about to show you has helped my students go from truck drivers to analysts and land roles at big companies like Microsoft and even score $20,000 raises in about 90 days. Now, if you're serious about getting hired in 2025, this is your blueprint. You better watch the video until the end so someone else with even less experience and talent than you takes the job that you're aiming for. Now, if this is your first time here, my name is Kedeisha Bryan. My name is Kedeisha. I used to deliver pizzas and now I deliver insights as a self-taught data scientist. And I've coached thousands of other career changers how to break into data without going back to school. And now I'm here to teach you how to do the same. If you ever want to work one-on-one with me to land your first data analyst role in under 6 months, click the link in the description so we can get you hired ASAP. Now let's get into it. Now, before we dive into the project, let me hit you with a hard truth most people don't want to hear. Most data analyst projects don't get people hired, and here's why. They're built for learning, but not proving that you actually have the skills. They use perfectly curated datasets with clear instructions. They're often way too clean, too polished, and just too generic. Hiring managers have seen them a thousand times. Netflix dashboards, COVID dashboards, superstore sales, all over GitHub, all over LinkedIn, and you blend in before they even click on your profile. But the people who actually get hired, they build projects that solve actual business problems, that mimic what analysts do on the job already. Dirty data, confusing objectives, unclear outcomes. That's real life. And that's what recruiters and hiring managers are scanning for. This project that I'm going to walk you through in this video, it's different. It forces you to act like an analytics consultant, not a student, and it shows you how to take initiative. Work with messy business data, and deliver clear insights that drive action. This is the same kind of project I've seen land my students jobs at Microsoft and Roku without another degree and more certifications. And the best part, almost nobody else is doing this, which means you instantly stand out. Now let's get into the only portfolio project that you need to separate you from everyone else and the so-called aspiring data analysts out there. This is a project that flips your resume from student to strategic partner. And more importantly, it gives you what 99% of people don't have— evidence. Because in this job market, nobody cares what you say you can do. They care what you've actually done and what you can prove. And this project shows them exactly how. And this is what I call the Volunteer Analytics Consultant Project. But let me be clear, this isn't volunteering. This is real business analytics, real decision-making, real deliverables, and a real transformation for organizations and for you. And no, you don't need to wait for someone to hire you. This is how you create your own experience. Now, I'm not making this up. This is the same kind of project my business school gives to students who are paying over 6 figures to earn this degree. And the same kind of project that gets those same students $150K roles after graduation. But I'm going to teach you how to do it yourself without the gatekeeping and without the debt. So let's break it down step by step. Step 1, reach out to a local nonprofit. Start with a Google search and find nonprofits near you. Food banks, shelters, churches, educational programs, any organization that runs on spreadsheets and is drowning in data that they don't understand. And don't overthink the outreach. Keep it it simple and direct. Offer them 30 days of free analytics support. Let them know that you're looking to help organize and analyze the data that they've got— donor lists, event attendance, surveys, budgets, whatever they have. You're not asking for permission to learn here. You're offering them value. Out of 10 outreach attempts, one will eventually say yes because they need the help and no one else is offering this. Step 2, treat it like a paid consulting project. From day one, act like you're on a client project. Set up a kickoff call, ask smart questions. What's the problem they're trying to solve? And what data do they have? And what decisions are they struggling with? Get their Excel files or Google Sheets. This will be messy, inconsistent, but 100% realistic. And then clean the data, explore the data, extract actual insights, not just stats, not just pretty charts, but real takeaways that they can act on. Once you've got the insights, it's time to build your deliverables. Your first deliverable will be a dashboard report that you can build in Excel, Tableau, or Power BI. And once you've got your analysis, now it's time to build your second deliverable, a slide deck. And this isn't just any slide deck. This is a consulting-style presentation that follows a pyramid principle. So what does that mean? You lead with the answer and you support it with key insights and you structure it to drive decisions, not just dump data. It's clean, it's strategic, and it makes hiring managers pause and say, wait, this person actually gets it. Now here's what it'll look like. You start with the objective. What business questions were you solving? And set the stage clearly and concisely. Then you move on to key insights. What did the data reveal? Present 2 to 3 bulletproof insights that matter. Then data visuals. Support those insights with one strong, uncluttered chart per slide. And then your recommendations. What should the organization do based on your findings? Then impact. Quantify the business value and show how your insights lead to real outcomes. Each slide should have one clear message, one purpose, and one clear takeaway. You're not just sharing information, you're guiding a decision. That's what analysts do in the real world. And when a hiring manager sees this, you go from just an interesting candidate to a must-interview. Step 3, deliver value in 30 days or less. Now, this part is crucial. You're not staying there forever. You're delivering results in 30 days or less. Why? Because this is about building proof fast, something you can speak about in interviews, add to your resume, and post on LinkedIn. And once you deliver that value, you walk away with a polished case study, a project to showcase on your portfolio, bullet points for your resume that actually matters, and a testimonial that proves that you are the real deal. If they ask you to stay longer, that's completely up to you, but your goal is to create a measurable impact quickly that you can add to your resume and speak confidently in interviews. Step 4, make it part of your brand. So once your project is done, don't just let it collect digital dust. Post about it on LinkedIn and you turn it into a clickable portfolio project. You practice how to talk about it in interviews. That's what gets attention. That's what gets callbacks, and that's what gets offers. Step 5, the proof is in the results. So let me show you what actually happens when people actually implement this strategy. One of my students went from driving trucks to landing a process analyst role in about 90 days by treating this project like a real consulting engagement. Another came straight out of college with zero job experience. But because they had this kind of project on their portfolio, they secured not one but two internships. And someone else coming from the entertainment industry used this exact framework, paired it with solid SQL and storytelling, and is now working as a data analyst at a Fortune 500 company. This isn't theory, and this isn't some bootcamp promise. This is what real execution looks like. So what are you really learning here? You're learning how to think like an analyst. Not just follow instructions and follow along some tutorial. You're building experience in data wrangling, stakeholder communication, business problem solving, storytelling with visuals, and presenting to decision makers. You're doing exactly what real-world analysts do, and not just what courses teach. You're building confidence, credibility, and leverage. The pros: immediate experience, low barrier, sets you apart fast. And it costs you nothing. The cons, you have to take initiative. You have to break out of your shell and reach out to these organizations. And you'll probably be uncomfortable at first, which is why no one actually does this. But the few who do, they win and they launch 6-figure careers in data analytics. So now you have the full roadmap, not just theory, not fluff, a real portfolio project that puts you at the top 1% of applicants. Without another degree and without waiting. And if you're telling yourself you still don't have the experience, now there's no more excuses. You just learned how to create your own. The only question is now, will you actually do it? Because the people who win in the market, they take the messy data, unclear problems, tight deadlines, and they deliver. That's what this project trains you to do. And that's what hiring managers want to see. And that's what gets you hired in this job market. If this video helped you, even just one light bulb moment, make sure you like, comment, and subscribe. Let me know what kind of organizations you're thinking of helping with your project. We're going to build a FIFA World Cup prediction project completely end to end, fetch our dataset, build the project requirements, do the analysis, the data cleaning, and a final actual portfolio one pager. What we're going to do is say, I want to create a data analytics portfolio project. Actually, you to help me to create a data analytics portfolio project, a FIFA World Cup prediction project in honor of the 2026 World Cup. I want you to source The raw data, create a project plan with a business problem. So we're going to always try to be like a business problem. We're going to frame it like a sports analytics project, like a sports data analyst that's working for the World Cup. Let's say that we're in Atlanta, for example, creating a project plan with a business problem, visualization of the tables, and Start from there. I don't want anything from Kaggle. Real data only. Do not start cleaning or analyzing until I say so. It's thinking strategize, sourcing real datasets. This is perfect timing. World Cup kicks off June 11th. So this project rides a massive cultural moment. Let me verify the current tournament details and lock down real data sources. Just like what I love with Manus, you can click on Claude each step and see exactly where it's getting its sources from. So I said no Kaggle and I can verify ain't no Kaggle to be found. So great. Got a full dataset of nearly 50,000 matches based off of historical data from 1872. I don't know about that. I mean, for the NBA title. NBA Finals, uh, the Knicks are in the finals right now. And I don't see how a team probability of winning in the 2026 World Cup, like what they did 30 years ago or something like that, I don't see how that would have any effect. All right. Everything is sourced. Here's a full picture. So we have a results table, 49,000 international matches from 1800. It already contains the 2026 group stage fixtures with empty scores. Okay. Shootout CSV, 677 penalty shootouts where draws don't exist. Goal scorers, 47,000 individual goals with minutes. A World Cup database. So here's this visual that I really like. This is cool. It says something about Elo ratings. You'll compute your own Elo. Before it goes any further, I'm like, okay, sounds great, but what do you mean? By Elo. I should say, explain it to me like I'm 5. You darn right. If you can't explain it, you can't defend it in an interview. That's right, Claude. Elo is a rating system originally built for chess players, now used everywhere in sports analytics. Every team gets a single number representing its strength. Say Argentina at 2,100 and Panama at 1,600. The system runs on 2 simple rules. Rule number 1, the rating gap predicts the outcome. Before any match, the difference between 2 teams' ratings Converts directly into an expected result. A 500-point gap means the stronger team is expected to win something like 95% of the time. And it's an equal rating, 50/50. So the rating isn't just a leaderboard, it is a probability machine. Rule number 2, ratings update based on surprise. So after a match, points move from the loser to the winner, but how many points depend on how surprising the win was. So if Argentina beats Panama, Almost nothing changes. That was expected. So Argentina gains maybe 1 or 2 points. But if Panama, the underdog, beats Argentina, that's a shocker. So Panama takes a big chunk. So maybe 30 points. Okay. Argentina's ratings drop. Beating up on weak teams doesn't inflate your rating. Beating strong teams does. Okay. So now here's why we're building it ourselves instead of downloading it. We start every team at a baseline. In 1872 and replay all 49,000 matches since then in chronological order, updating ratings match by match. And by the time they reach June 2026, which is right now, every team has a strength score earned from over 154 years of actual results. Okay, now this is making sense. So again, we see something that was like, what the heck is this? And then we ask and we get an answer and it's breaking it down in such simple Simple terms. So again, so by the time we reach June 2026, every team has a strength score earned from 154 years of actual results, and we get to make real analytical decisions along the way. World Cup matches should move ratings more than friendlies, which is a K-factor. Home teams need an adjustment, and a 3-host tournament takes home advantage. So the payoff: Elo becomes a backbone feature of our prediction model, and we can directly test it against the official FIFA ranking. In Phase 4, spoiler, FIFA's ranking is famously bad at predicting match So showing our own rating system that outperforms FIFA's official one. Okay. Can Fable 5 help us outpredict FIFA's prediction? So let's look at the project plan that Claude Fable created for us. Project code name, Road to the 19 July Tournament Window. 48 teams, 12 groups, 104 matches. Okay. Business problem. You are a data analyst at a sports media company preparing its editorial, advertising, and sponsorship Strategy for the FIFA World Cup, the largest in history. But leadership has a problem. With 48 teams and 104 matches, they cannot cover everything. Ad inventory pricing, content production schedules, and sponsorship pitches all depend on one question. Which teams are most likely to go deep into this tournament, and how confident are we in each match outcome so we can allocate coverage, budget, and inventory where the audience will actually be? Our stakeholder is going to be head of content strategy. Our secondary is going to be VP of ads decisions that this analysis drives, which 10 to 12 teams get dedicated pre-tournament content packages. This is a big thing, right? You don't want to spend our ad dollars and stuff like that on like the teams that are not going to get, not expected to go very far, for example, right? Anytime there's like some sort of tournament, any sports folks, like the NCAA basketball tournament every year, the NBA playoffs, for example, NFL playoffs, like You know, you always see the players getting interviews, the teams getting the most, you know, in the commercials, really the top ones that are expected to go far. So that's pretty smart, right? Which group stage matches are priced as premium ad inventory? True. Which knockout pass scenarios to pre-produce content for? A published falsified prediction bracket content that gets graded in public as the real tournament unfolds. So that's pretty cool engagement. Gold right there. Sort of like back when like the presidents used to predict the NCAA tournament, right? When ESPN used to go to like Barack Obama's, you know, in the White House and he would literally, they literally have the whole episode of him predicting it. So that's pretty cool. That's an engagement thing, right? So project phases, phase 0, sourcing and verification done. Identify the primary sources. No Kaggle. It's coming up with all the different stuff. This is really good. Okay. Go to the next step. Notice that discrepancy in the spelling. It realizes the same country. So, all right, let's see what we got. All right, so we can look at what the data looks like. These are groups, whether they're the host or not. Then we have FIFA ranking pre-tournament top 50. So this is the ranking top 50. Argentina is number 1, Spain 2, France 3, England. Portugal. Okay, so these are the rankings. Okay, let's go to the data cleaning step. But first, analyze the data and flag all data cleaning issues before actually fixing. So we don't want it to just start fixing things. Let's have it analyze and have it tell us what it sees, and then we can decide as well What to do, what not to do, what they recommend. We want Claude to do an audit, tell us what it sees first, and have it give us recommendations. But we don't want it to like, oh, hey, just go clean the dataset first. Not— yeah, yeah, we're managing the AI. You have to remember, the more capable the AI is, the more QA and the more management you have to do really. Right. So AI agents or AI, it's very helpful. It's very effective. You can definitely leverage them to to 10x your productivity and your income as a data analyst. But with great power comes great responsibility. If Claude went out and messed something up, Claude ain't getting fired, right? You're getting fired. And again, we can always click on there and read exactly what it's doing. Going to create an actual data quality report, which is great. Let's look at our audit report from Claude Fable. All right. It created different keys, right? Critical, something that can potentially Break or corrupt the model because remember we're building a predictive model here. High wrong analytical conclusions if unhandled. Medium limited scope handle with a documented rule verified. Okay check no action need. All right critical stuff that I found team name chaos across sources. The historical FIFA rankings file uses 36 team names with no exact match in our results CSV for our 48 World Cup teams alone seven break. On at least one join. Dang. South Korea, Korea Republic, United States, USA. Okay, well, United States equals USA. Carousel, Iran. So it looks like it is variant in spelling that I'm seeing. Okay, there's different. All right, so there's different spelling. Four spellings in one file. Proposed fix: build one name crosswalk table and map every variant. I think that's a good idea. Because it's just a naming thing. I think that's a great idea. All right. Next one. Era split name. Same country, 2 identities over time. Within the ranking files, countries change names mid-history and splitting their rankings into 2. Turkey to this one. Czech Republic to that one. Cape Verde Islands to that one. Switzerland to that. FYI, Macedonia to North Macedonia. The risk here is point in time ranking joins silently. Returns nulls for the other era. Part of me wonders how far, because this goes back to what, 1800s? I don't think we got to go back to 1800s, to be honest with you. We can— I feel like we can like limit the scope, shrink the scope a bit. Let's say the past like 4 World Cups or something. What happened in 18-whatever is not going to affect what's happening now. I'm a basketball fan and the New York Knicks, they're in the finals right now. I don't see how like having like a scoring system from the 1970s. I, I just don't— just in terms of just because of like differences of— I mean, I just think it's ridiculous, just off top of my head. So let's just go back to 30 years instead of the 1800s, especially with all the issues with spelling and, you know, stuff in history. So let's just do 30 years. We're doing the Women's World Cup inclusion? Oh, I didn't realize we're doing both. I don't I don't recall them mentioning anything about the Women's World Cup. Data alignment. FIFA rankings begin 1992. 26 releases already exist before our window opens, so every match in scope has a point in time. Statistical sufficiency, 28,000 scored matches, active national teams average. Pre-1990s international football differs structurally. Fewer fixtures, no Nations League, different qualification formats. All signal is a weak signal, 2026. You dang right. Seems like there's still a lot of names that are— um, let's just focus on the men's tournament and let's do your D2 recommendation. Yep. We're gonna filter matches to just the men's tournament. So perfect. We're on the same page there. Just the men's tournament. FIFA members only. Nice. All right, so that was good stuff there. Yeah, so deciding on limiting to 30-year cutoff definitely helped a whole lot. And now we have to just standardize or basically like United States equals USA and things like that. Those are 2 different countries. If we go back to the teams that are like number one or because ideally we want to do a prediction of the winner. If it was like Portugal or whatever that was in a lot of these like issues or data quality issues, that'd be one thing. But like A lot of these data quality issues are, are with countries that are not expected to win. Okay, so phase 2 is done. Data is clean, validated, and split. So what we got so far, we got 29,000 scored training matches, 72 prediction target fixtures, every team a FIFA member with a complete ranking history, zero duplicates, all 48 cup teams joined cleanly across every file. Because we pretty much matched the different naming conventions. 2 things worth bragging about, right? The entity filter caught over 100 non-FIFA teams, right? And we don't, we don't want that. And every transformation is logged. So nice. Carry forward, right? So we have, let's see, we got our log here. Yep. So this is basically everything that we did. This is nice. It's good to have this. Matches is our training data. Okay. Fixtures. All right. Feature engineering. We're going to build our Elo engine from scratch. All right. So let's go. All right. Our Elo models perform well above baseline with 76% accuracy, already outperforming FIFA's ranking system by 3.7 points in our head-to-head comparison. Okay. Now I'm checking the data quality. We got 99% Coverage. Carmel's already performing better than the FIFA's, so that's nice. Okay, so they replay all 26,000 matches in chronological order from 30 years ago, every team starting at 1500. And as you recall, though, how the numbering system works, so your scratch-built Elo beats FIFA's official ranking, right? On decisive matches since 2000, the team with the higher pre-match Elo score won 76%. The team with the better FIFA rank only 73% of the time. There's a 3.7-point edge over the official system before we even trained a single model. That's good. All right. So let's do some exploratory data analysis. Yeah, let's do some of that. All right. So the most important thing here is the prediction, but let's see what this EDA is looking like. When our Elo and the FIFA ranking pick different favorites, how often does the team, better team actually win? Home advantage is real. Finding 1, home advantage is worth— okay, home teams win 50% of non-neutral matches versus 41% of neutral matches. Finding 2, the upset curve is smooth. Favorite win rate climbs steadily with an Elo gap of 39%. Upsets fall from 32% to 2%. When our Elo and FIFA's rankings disagree, Elo wins 60-40. All right, so when our 2 systems picked 2 different favorites to win, and about 17% of the decisive matches. Our model beats FIFA's. Nice. Okay, next let's see the prediction model. We are gonna pretty much simulate the World Cup, see who's gonna win, predict who's gonna win. All right, 2022 was one of the biggest upsets— Saudi Arabia beating Argentina, Morocco. Okay, our prediction is saying that Mexico We'll have a 76% chance to beat South Africa. USA only has 40% against Paraguay despite being on the home soil. All right, so we're gonna simulate all 104 matches. So this right here is just, we just came out with, um, this just built what we have here is just the, like the probability of the home team winning. So Mexico, 76% for this match, South Korea versus Czech Republic. South Africa has a 46% chance to win. And then there's a 26% chance of a draw. Okay, let's keep going and let's simulate and see what we're actually going to predict. Claude Fable is going to run our model 10,000 times and we are going to finally see. Oh, token constraints. Okay. All right. It's doing a mapping of the bracket structure. You don't want the model to predict matches that, you know, that just Will not actually occur logically. So that's smart. One thing I've noticed, it is going through different website articles and actually reading through them, doing the research, and you can just see its logic per se. All right, I'm noticing something off with the United States reach probability at 70%. That seems too low given their group composition and the fact that 32 of 48 teams advance. Uruguay's at 87%, which Makes the gap seem suspicious. Yeah, especially since the U.S. only has a 40% win rate. The numbers are in and they're spicy. Let me chart the title odds and write the forecast. Okay, so looks like Claude Fable has finally finished. Our prediction is Spain winning. Our title odds is Spain winning, basically. Spain, Argentina, top two title odds. Spain, Argentina, charts, paths. Want me to drop a Tableau-ready export? Um, no, I want it to— dang, 90% of the session limit. Okay. Finish off with creating a one-pager portfolio. Make the visuals consulting style. Think Boston Consulting Group, so BCG. And McKinsey include the visual of the table schema, a visual of the data pipeline, and a final dashboard. All right, let's see. Can Claude Fable do this for us all in one swoop since we we hit 90% of our session limit? What? 5-hour limit? Oh my gosh. Our Jowell Fable is expensive. That's number one. As we can see, I saved $50, but I paid $200. No, I'm not doing auto. Come on. Set monthly limit to $2,000? What? No. And the nerve to even suggest $2,000. All right, here's our one-pager. It's in HTML. See, I'mma publish this artifact and anyone will be able to see it. Official ranking system built from predicts of FIFA. The only thing is like, it should say it predicted Spain up here, but down here is fine. This is very cool. I'm going to link this in the description. I will say when I did the, the one-pager, tested this in Gemini Spark. It felt like I was going back and forth with it for like 20 minutes to try it and build like a one-pager in like a public website. And it would not be able to do it, which is crazy because Gemini has Canvas. And for whatever reason, Spark wasn't able to use Canvas for a reason. But in terms of publishing an artifact or a website, now I can link to my resume. This is good. This is cool. So I'm going to link this. This is going to be an artifact that I'm going to link in the description. I, you did just see me pay $200 to get more credits. I had to rank, you know, the AI models of the Fable, the new Gemini Spark, and Manus. I did like Fable's reasoning, you know, it's thinking and things like that. I still like Manus off the top because Manus, you can, you can actually talk to, send prompts mid-prompt. But this is one of the best outputs in terms of like when I say, hey, create me a one-pager. And I just said the, you know, Consulting style. This is actually really, really good, but expensive. I don't, I wouldn't say it's worth what I just paid for. Like, that's kind of crazy to spend $200. We used AI to do a, you know, really nice data analytics portfolio project. As you saw though, anytime there were questions or things brought up, like Claude said, hey, we're going to do this. We're going to use ELO, whatever. Step in and say, what the heck are you talking about? I don't know what that means. Explain it to me like I'm 5 so that while you are utilizing AI to build high-value projects, you're not just having it just do things. You're asking questions, you're looking at the data, you are having it, you know, perform audits. You have Claude or AI provide you recommendations, but you are still coming up with the actual solutions on what to do.

We're going to learn how to build our own portfolio without any code. And honestly, it's probably going to take probably 20 minutes. If that, uh, we're gonna build it with Canva today. So I'm gonna show you guys first, um, all the fun stuff Canva has, and then I'm just gonna share a template so that we can just kind of get started. But just waiting a little bit more, a little bit more time. Um, if you guys have questions, we will have a I'll hold a Q&A or so at the end too. But if you guys also have questions along the way, please post them and I will answer. So this is Canva. I'm sharing my screen with an incognito browser. So, okay, good. Everyone. Okay, good. Ones, ones. Good, good. You already have a Canva account? Good. This is going to go a lot more smoothly. Canva's free. All right, Kendall, um, just go to canva.com and see a— let me see, I don't think that's gonna post to LinkedIn. Let me post that to LinkedIn. Uh, it's just canva.com. Oh, okay, Donald, you've used it but don't have an account. Okay, if you don't have an account, super easy to get started. So canva.com. That's canva.com. And then we'll just go to sign up and you can just continue with your Google. I just, I use, I have my Google, so you can just continue with your Google and you'll, it takes like 20, you know, probably 10 seconds to get started. But for those who've never, who do not have a Canva account, just go ahead and do that. Take like 10 seconds to do that. And then when you're good to go, just then press a 1 or just press or type, you know, good to go. Good to go. We will be using Canva. And I want to show you guys all that Canva has to offer before I share my template. But you do need to have a Canva account to use this template. And it's, and it's free. Okay. So first, let me show you guys all that Canva has to offer in terms of templates. So there's a search bar up here, right? What I want you to do is press or just type in portfolio. Just press portfolio. And then you'll get to this page here. There's going to have, if you have designs yourself, Your designs are going to come up here, but then all the Canva templates are going to come here. As you can see, there's 18,000, right? So what you want to do is go to See All to the right side, click that, and now we have all the Canva templates, right? 18,000. All right, let me— you guys, are you guys here? Because once you're here There's a thing called filters, all filters. There's multiple categories of different type of Canva templates. Uh, what we want is a website. So from all categories, click website, and then once you're there, you, you're gonna click apply. Uh, if My cat is kind of being crazy in the background. If, if, uh, if it's too loud, let me know and I will kick him out. All right, if, uh, if he's too loud, press cat or type in cat and, uh, I'll get him together. Um, otherwise, let, let me know if you guys are good to go, uh, at this step here, because once we click apply, then it's gonna change. But are we good to go so far? Once you have website clicked in categories, you just click applied, and now you have all these type of categories for, uh, Canva sites. Um, now one thing to note, if you look, if you notice on the bottom right, there's a little crown on some. Those are ones for Canva Pro. Now, Canva Pro is like, I don't know, $10 a month. Honestly, every person, every professional, there's some people in here trying to get their first job in data and, oh, okay, but yeah, we'll save the cats, Scott. There's some of you who can't find website template. Okay, is everyone else okay? So let's go back. I'm gonna go back to the first page. I'm gonna go back to the home page. I'm gonna go to the search bar and I'm gonna click— I'm gonna type in portfolio again. Okay, and so we are at Canva, right on the— down here it says Canva templates. So where it says Canva templates, can't find Website template. Hmm. Who else can't find website template? We might have to do some troubleshooting. If anyone would like to get some one-on-one troubleshooting, let me know. I will invite you to the studio. But I want to make sure, um, I want to make sure we get this. If you want some one-on-one troubleshooting, Donnelly or Kendall, I will invite you to the studio so I can help you troubleshoot. But it's, uh, it's basically a category. So you could also do— if you— there's category here right below the search bar. So you can also click website here. Kendall and, um, Donnelly, are you seeing that as well? So there's, there's a couple options to get to the website, but it's just a category. You can also type in website. Yep, you can also type a website, but we also want to make sure that category is— for category, we have website checked. Now see and see all. All right, who wants to, uh, all right, cool. All right, let's just do that too. Website, still go to Canva templates. Um, but you want to still want to make sure website for categories is checked. It's different if it's not. If it's not there, then it— that completely throws it off, right? Because we want to create our own website. So is everyone good to go on website being checked for categories? Because otherwise you're not going to have the It's not gonna be a portfolio site, it's just gonna be a PDF. Donnelly and Kendall, are we good to go on the website category? Now either way, um, I just want to share Canva has multiple, multiple, so many templates for your website. So again, I'm gonna share a template for you guys to get started. But you can just roam around and see all the different type of sites that you can do. So for example, this is one. If you click on it, you can preview it. You can kind of, you can scroll down to see what it would look like. And all you would do, all we're going to do is just plug, you know, plug and edit. So if you're interested in like, say this type of portfolio site, My website never appears as an option. Okay, now, yeah, Kendall, if you want to, um, if anyone wants some, uh, I'm gonna type, uh, I'm gonna share this. And Kendall, if you want, uh, some If you want to get on here, enter the studio to so I can see your screen. I'm going to type in the chat. Anyone can actually join, and then I'm going to actually pin this. All right, Kendall, you good? Okay, perfect. I'm going to show— I'm going to just put this in chat regardless if anyone wants to hop on. And then if anyone wants to hop on into the studio and like share what they have at the end, that is the invitation. Okay. All right. So this is again, so this is all the website portfolio or website templates Canva has to offer. It's over 3,000. And this is just by typing in website. You can type in, again, you can type in, say, portfolio, whatever. But as long as the website category, that's really the most important part. So like this one, for example, this is one that looks really good. It's responsive too. And you can just edit these to design your own website or design your own portfolio. So many things. So this is, this is a lot of good things you can play with here. Now, I'm going to share a template with you all that I created for the time being. And let's see, I'm going to share a template. Template link. Okay, what I'm gonna do is I'm actually going to shorten the link for you all, and so that we all have the same starting point. Are people still having the issue of not having a website option? Anyone else still having that issue? Okay, good. I want to also ensure that folks who don't have Canva Pro still, you know, has access to the website option. That should— that's not a pro type of thing, so it's not a paid category. So I'll go back. There's a couple ways you can get to the website option. So let's say I'm just gonna— again, I'm just gonna type in portfolio, whatever, at the top search bar. Right where we have filters right below the search bar, go to category, and you should see website here. There is no crown next to it, so it shouldn't be a pro category. So everyone should have that option. In the meantime, though, I did post or I did share the link to the template that I created for the webinar for today. And as long as you— again, as long as we have a Canva profile, you should be able to copy that and be able to— okay, so is everyone good to go on accessing the template that I shared? You should be able to click on that or pretty much copy or type out that— type out that link Or type out that you know website link I have on the screen, and then you should be able to like make a copy, and then you should be able to be in here so it can be edited, so to speak. Put good to go G to G if you got through to the template and you are there to edit it. Let me know. But let me know if you were there. Not seeing. I only see Edith says good to go. Anyone else good to go? Is everyone? Did everyone else? Did everyone else write down this link I have? At the bottom left, I also I also posted it in the good to go candles. Good to go. Good, good, good. Everyone else work though. It's right on the bottom left of the screen that you're looking at. Https colon two forward slashes bit.ly forward slash three d k i h v m Right there. I posted it in the LinkedIn chat, but I'm not sure if you're on— if you are on YouTube, it's also there. It's probably easier to access on YouTube because— okay, everyone's good to go. Are we all good to go? I don't mind waiting a little bit because like I said, I— once you got— once you got the template, It literally takes like 10 minutes to really finish. Sorry. Okay. Okay. So for those who are here, it looks like most, most of us are here. Um, if anyone needs to catch up, I'll go back. But this is a template again to get started. This is actually a template that I give to my students when they sign up to my program. So I have a program that helps career changers. get their data job, get a data analyst job in less than 6 months without going back to school. So it's a one-on-one help. We meet with them one-on-one every week. It's very personalized. And on the very first onboarding call with me, what I do is I set them up with first starting to create their portfolio. If they already have one, there's also some things that we do, but This is— I'm going to pretty much simulate the onboarding call that I do with my students to everyone here. So the first thing is, if you notice, basically the first page is going to be a photo, your, your name, your target role, then links to your email, your resume, and also links to your Tableau, LinkedIn, and GitHub if you have one. And then an About Me, and then of skills. And then down there, if you scroll down, these are where you're going to be putting data analytics concepts. So let's say if you did, you know, if you did, you know, market basket analysis, if you did, you know, cohort analysis, etc., you'd write those here. And then you want to summarize your project and say 4 bullets, a 4-bullet summary, a 4-step summary, but summarize in 4 bullets. Sometimes I see people, they have these like long drawn-out like blog posts type of thing for their project write-ups. No one, no one has time to read all that. So just summarize it here. And then I have just 2 places where you can just post or insert photos of things in your project. So good thing we can do here, let's say for example you're doing an EDA project or something in Excel or Python or whatever, you can insert a couple images of certain visuals that you did and you know, in your, in your project. Um, the second, uh, the second variation is again same thing on the left side, but this is one, say if you have a dashboard, and then you can just insert a photo of your dashboard in this one here. And then let's say this is like a continuation of this one of the dashboard project. So you have a dashboard, but you know, you want to, you know, probably get— if you have a dashboard, you've done EDA, you have like multiple, say like multiple visuals, you can have multiple visuals here, you know, insights, and then explain your visual down here. So very, very plug and play, easy, no coding necessary. So the first thing that I always have people do is, again, obviously you can put your, your picture in here. An easy way to do that is you go to your LinkedIn. I'm going to go to my LinkedIn and show. Go to your LinkedIn, click on your image, your profile image, right-click and copy image. Then we're going to go back to our portfolio and then paste it. Crap, no, don't do that. Well, yeah, paste it. Then when you do that, you just click on it and drag it into the circle and it's just automatically going to populate. Did everyone see what I did there? We're just copying our LinkedIn profile photo, pasting it back into our, um, Portfolio and then dragging it into the circle where, where are my images, and then it automatically just populates. Did everyone get that, or was everyone good to go on that? Thanks, Scott. My career would have gotten a lot further and a lot faster if I had Kedisha's program back in the day. Uh, yeah, Scott, that has no financial interest. Can't recommend it. Thank you. Absolutely. It's all about accelerating in this, uh, accelerating career changers' careers, um, in this very difficult market. It's a very different market. Um, okay, good. Good to go. Everyone else good to go? Y'all are good to go. If y'all got here, y'all got your— if you have replaced my image with your LinkedIn profile photo, press good to go. Also, anyone who needs like a new profile, like a new professional headshot, I'm also going to share a resource where You can get that done today as well, because again, this is like your very first impression. You want to make sure you have a nice picture where you're smiling and friendly. All right, so is everyone good to go? Have you replaced my photo with your photo? Are we good to go there? I only see one good to go. Any— no one else's? Do I need to Do that again. Don't need to do it again. You're just all right. Kendall's good to go. Perfect, perfect, perfect. All right. Edith is good to go. Perfect. Yep. Jessica's good to go. Perfect. Okay. Good. Okay. Good. Okay. And then obviously. You, you replace your name, right? So when it comes down to editing anything in Canva, as you can notice, if you hover around, everything has like a container, just like Tableau. Everything has a container. And all you would do is double-click inside the container and just replace first and last with your name. So you literally just do— I'm just going to do my name. And then you'll do your target role. I'm having to look for another picture, don't want to use the one with it. Oh yeah, that is true. That is one thing about when— if you have the, if you have the banner, um, it, uh, it does copy. So yeah, if you, um, so yeah, that is, that is to note. So definitely, um, use, uh One without the banner. And then you put your target role. So whatever your target role is, data analyst, data scientist, if you are— I know some people in here are more up the ladder, director, you know, CAO, manager, whatever your target role is, put it there. I'm going to put data coach. Okay. So your name, your target role, and then here, this is something where you definitely want to pay attention. So you can insert links with Canva. I have 2 specifically here for your email and your resume. So if you click or double-click where it says email me, Right now it's going to have my email. Please don't flood me with emails. But what you would do is you're just going to edit link. Where it says mail to, keep that there. But you're just going to replace my email with your email. And again, please do not spam me with emails. This is my actual email. But you're just going to replace that with your own email. So I have my email there. You're just going to replace it with your email. Because whenever people click on it, automatically it's going to populate with their email service. So if it's like Outlook or whatever, it's just automatically going to populate. So all you have to do is click, double-click where it says email me, hit the edit link, the pencil, and then replace my email with yours. And then you're just going to click done. Now with the resume, I'm going to do the same thing. Now, here's what you want to do with the resume. So I want you, you all to, you also have a resume ideally in a PDF format. And all you're going to do is upload it onto Google Drive. So go to your Google Drive and just upload your resume. You're just going to upload your resume. File upload. Find your resume. I did a new one last night. You're going to upload a PDF of your resume over to Google Drive. You're going to find it. You're going to share. Go to the share settings. In general access, just make sure anyone with the link can access it. Make sure it's just that viewer. Click done, and then you're just going to copy the link. Wherever that link is, I'm going to go back and then replace that link there with that new link to your PDF of your resume. Do I need to go back and do that a second time? I know that was a bit of a few steps. Essentially, all we're doing is taking our most updated resume. As a PDF, uploading it to Google Drive, changing the share settings so that anyone with the link can access it, then copying the link and just editing it or just replacing it in the resume box. Will this be recorded? Yes, this will be recorded. Yeah, this, this is being recorded. So yes, this will be shared. If anyone missteps, but that I would say this this is probably the most step-intensive portion. Just replacing the links that I have. Essentially, again, we're just replacing our email and resume with yours. So now down here, this is where. So this is anyone with Tableau. If you click on the Tableau. Uh, icon and you click edit, you're just gonna replace my— this might be an old one— you're just going to replace the Tableau Public link with yours. Same thing with the LinkedIn one. If you double— if you click, click the pencil, replace my link, replace my LinkedIn, uh, URL with yours. And if you have a GitHub Again, just replace my GitHub link with your link. If you don't have a GitHub, or if you have Dabi Pro or something like that, um, you can just replace, um, you can replace the— you can take away the Tableau link. And what you can do is if you go on the left side, you go to Elements, you can add elements. So let's say you want to add a dashboard. If I do dashboard, I'm going see graphics. I click graphics again. Anything with the crown is for Canva Pro, but anything without a crown is free to add. So let's say I have Power BI. I don't have Tableau, so I don't want to have the Tableau icon there. You know, I can say, oh, actually, you can— let's see if the Power BI one is actually free. Oh, look at that. There's a Power BI icon that's free. So I can take that, minimize it. And if you have Navi Pro or, you know, whatever— most likely Navi Pro, right? Same thing. You'll click on the button, go to More, and then click Link. And then you'll just enter your Navi Pro link, whatever public link to access your Power BI dashboards that you would do. That's what you would do there. Um, hopefully people are good to go on that. That is if you have Power BI. For those of us who are Team Tableau, right, if you're on the right team and you're on Team Tableau, then you, you know, you're good to go there. All you have to do is just edit. Um, show the resource for the professional headshot. Yes, I will show the resource for the professional headshot right after we're done with the Project 1. I'll show the resource for that. Okay. Now moving on to the right side. So one thing that I do with my students in our onboarding call, I want them, everyone should have their own elevator pitch, right? Um, 20 seconds, like, why, like, why would you, if, if you were in front of a hiring manager and they had a job in their pocket, like, what, what, what, what, what exactly, how would you, you know, showcase yourself or how would you explain why you would be a great candidate, you know? And basically the about me is the elevator pitch, right? So you're, you're basically going to replace mine with Your type of elevator pitch. Let's say you don't really know what to do with that. What you can do is literally you can take your resume, you can upload it to ChatGPT, and just ask it to, hey, write, you know, take my resume or analyze my resume and write a kind of like an elevator pitch or like a professional summary or something like that. And then obviously you don't want it to be too long, so just Make sure you keep prompting it to make it short, or or you can go to your if you you know everyone should have their about me. Your about me is also like your elevator pitch, so you can also copy and paste your about me and do the same thing. If you yeah, so Eric Sims has a great comment here. Elevator pitch. The tell me about yourself has guided interview conversation. It's a great opportunity to shape the interview to work for you. That is true. The tell me about yourself question is definitely the most, probably the most scary one. Because let me hide my— the part of the most scary question, because no one likes to talk about themselves. People like to downplay their abilities and stuff like that. But that is That is, that is a question where you are going to be your, your, your biggest advocate. Um, and essentially your About Me section on your LinkedIn and your, and our About section here, it kind of does the same thing, right? This is a, this is like your elevator pitch. So if you don't have one right now, everyone should have a resume. Uh, maybe your About Me isn't really that great or isn't optimized yet on your, on your LinkedIn, just upload your resume into ChatGPT and say, or if you want to copy and paste it, whatever it is, and just say, hey, analyze my resume or analyze my, my background and write a, you know, 4 to 5 second elevator pitch and just, you know, edit that down and put it here. Okay, so that's, that's the About Me section. Uh, then you have Skills. So Skills, pretty easy, pretty easy to understand. You're just gonna write your skills. Uh, so on the left side you can maybe focus on more hard skills, on the right side you can do more soft skills. But, um, this is gonna just replace all of these with your skills. So, and replace them with, you know, place them what you, what you have right now. So when my students on the onboarding call, um, you know, if they've never taken SQL before, we're not going to put SQL. You know, if they've never used Tableau before, we're not going to use Tableau. But we're gonna— we're going to write down the skills that we have right now, um, because the idea is as you do projects, you fill this out. You know, you document what you're doing. So again, skills, you just copy, you just double-click And just you know replace SQL and just type it. Very easy, very plug and play. Okay. All right. Is everyone good to go on the first page? So that that was that's that's our first page of our portfolio. Is everyone good to go there? And is everyone ready to get to the meat? and potatoes of our portfolio. Okay, right after I cover just project 1, or just the project pages, then I'll share the, um, the headshot, the professional headshot, um, resource. But is everyone good to go on page 1? Press good to go to let me know that we're ready. Let's move on. I don't see no good to go yet. It tells me are we stuck? Where are we stuck? All right, Jessica says we're ready. All right, so so when it comes down to project write-ups, I'm going to actually share. A, like, probably one of the best project write-ups I've seen, or just one of the best portfolios I've seen in a while, actually. This was actually from a previous supervisor that I had. If you guys look up Crisper Nelson, right? And you go to his feature. This is his, this is his, um, feature. This is his, um, portfolio, and it's just a PDF file, but his write-up is what's very, very good. Let me see if I will open it up. Okay, so it's just a PDF, right? This is— it's not a website, it's none of that, it's no Bells and whistle, but it's a very good write-up. Very simple and very effective. Purpose, concepts, tech stack summary process, and then pictures. Pictures on the right side. Just look how pretty and effective that looks. Pictures of the process and different types of pictures. That's essentially what what what what a portfolio is all about. A portfolio is all about. Showcasing your skills. And as a, whether you're, if we're trying to be a data analyst, we don't need to have fancy websites or anything like that. So again, this is just a PDF and it looks good, but the write-up is really well done. But I would say the pictures on the right side also really amplifies it. So I pretty much, the template that you have, I pretty much use this as a very good kind of guide or kind of, you know, kind of guide to it. Because like, again, it's good. It's pictures and then the process. Now you take away the pictures, it's just a bunch of text, right? It's a double, it's a one-two punch. You gotta have two. But it's not, maybe because of the way it looks, it doesn't Like it's all in one page. You don't have to scroll down like a blog. You can consume everything in one glance. So that's really the power of what a really good concise portfolio can look like. Now, again, you can just go on Chris's page and download or just look at it. It's in the featured section. If you, any of my students, I've shared this in our Discord. In the Slack channel as well. But that is basically the— that was basically the inspiration of basically this, the project write-ups that I have. All right. So there's a couple of different types, right? Or a couple of different sections. You write your project purpose, simple, your tech stack, what tools you use. So if you use Python, you use Excel. If you used Tableau, Power BI, you put it here. Concepts of the areas of data that you did. So again, if you did like cohort analysis, if you just did like, if it was a data cleaning project, like heavy data cleaning, you know, if you did like time series, whatever it is, you put it here. And again, probably the one most important thing, summarize it in 4 bullets. or 4 steps, a 4-bullet summary or 4-step summary, whatever you wanna do. But we don't, again, the main thing is we want the reader or the hiring manager to be able to consume or get the gist of our project in one glance. There's no, there's not a bunch of scrolling you have to do, all in one glance. And so to add pictures, I'm going to use I'm actually going to use. Let me go to my Tableau public page or something. Actually, all you have to do to replace the photo is same thing how we replaced the profile photo, but all you have to do. Is, and I'm going to try open up one of my projects for my students and maybe open up a workbook. Actually, yes, I'm going to share the. All right, I'm gonna I'm gonna share the resource. About that. Um, second. Wow. Okay, so. Let's say I have a— all right, so this is— let's see. All right, so this is just a Tableau project. Key Insights. Dalia asks, what about Key Insights? What about Key Insights recommendations? For instance, if I analyze a crime set, want to give recommendations based on findings. Okay, perfect. So let me get let me get to that. This let me get to that. Um, so uh Donnelly. So what you can do if you want to do recommendations, um you could probably remove purpose or or either probably remove maybe concepts or something, or you can you can replace The 4-step summary and process and just do insights and recommendations if you'd like. Yeah, so this is again, so you guys are like, I'm giving you guys a template. If you guys want to replace summary or what you can do is if you have space, you can copy that. And you can do insights and summary if you want. If you want to remove purpose or concepts, you can do that. Again, you just don't want it to be wordy. So key insights is good. So one thing I will say, these pictures here, so I'm going to go back and do actually, all right. So this is just a project that I did with my students, a live project. So let's say, for example, I want to do— we're talking about insights here. So let's just say I want to take— use this photo or this visual as a key insight, for example. I want to save this. I'm not sure how to— I can't remember how to save for Tableau ones, but I'm just going to take a screenshot for the time being, right? Just for the time being, just for to show. I'm gonna take that. I'm gonna save it. I'm just gonna save it to my computer. No, actually, no. I'm just gonna copy it. I don't have to save it. I'm just gonna copy it. And then let's say I want to paste it somewhere. I actually don't have to replace it with these images. Let's say I want to post it in here as a as an insight. I can do that, and then write a like a description below that. I can copy this. I can paste and just write say like an insight on you know most calls. Happened on— what is that— Tuesday. So that's the way you can pretty much talk about like individual insights in a project. That kind of dives into this second variation that I shared. So for this portion here, for Project 2, you would just paste your dashboard, and then for further analysis, you can again paste individual, like, you know, pictures and add your insights there. But again, you can replace the 4-step summary or you can replace the concepts if you want and add your insights and go from there. But it's the central thing about this webinar is that you have, you guys have free rein to do whatever. But it's a great starting point. So again, you can just edit these if you want to replace any of these recommendations, you can. But at least you all know that, you know, you can, you can get this done in literally like 20 minutes. If you all have anyone who's been doing projects already, you can easily just fill this out with your best 3 to 5 projects in like 30 minutes. Um, and, uh, yeah, any other questions? Like I said, it wasn't going to take long. Like, doing anything working with Canva is super quick, super, super quick. Any other questions? We are almost at 13 minutes left to the top of the hour. So any general questions or any other questions? Anyone want to share? that they have so far. Oh, before I get asked again, so let's say you don't have a professional photo. Listen, save— do not go and, and, uh, go to your local, uh, go to Secta AI. Let me go to secta.ai. $49 and get your professional headshot. I'm telling you, it is worth it. It'll be the best $49 you've ever spent. Best $49 I've ever spent. You need a good professional headshot. You don't want to have a, you know, do it— if you do it yourself, like, if you know how to make it look good, that's fine. But you want to have a good professional headshot that makes you look professional. This is a very, very good website. There's many like AI photos or whatever photo sites. This is very good. I recommend this all the time. I recommend this all the time. It's just secta.ai. I'm going to post it. Chat. See, and I'm going to sector.ai. So for your professional headshot, again, the best for— and you get like, I don't know, you get a lot of photos. Do they own copyright? As in, they— what do you mean? I don't Do they own copyright, as in they can use your image? Um, I'm gonna say no. Honestly, when it comes down to copyright and AI, listen, that, that is way over my pay grade. If you've been using ChatGPT or any AI at all, um, I mean, that's That's way above my pay grade. I'm going to say no, I don't think so. You're welcome, Catherine. Thank you for showing up. Thank you for showing up again. Like I said, the template for those, again, it's very— like I said, it's going to be very straightforward. It's going to be super straightforward. That's why I took the time to, like, if anyone was stuck, It's super straightforward, plug and play. If you want to replace any of these text boxes with resources or not resources, but like recommendations, you could also copy this, bring it up, and then copy that and actually have probably more space for recommendations if you want just a little bit. But yeah, there's a lot of things you can do. It's very plug and play. I recommend just in general, again, there's way more. Okay, Scott answered that. Thank you. Scott is an AI expert. AI images cannot be copyrighted by law. Good. Perfect. Okay, so again, plug and play. If you've never used Canva before, Um, this is very easy to use, um, and I, and I showed you at first of all the different types of websites, uh, portfolio sites and portfolio templates that's out there. Yes, it will be available. The recording will be available, Emily. Um, it will be available. I'll probably I will. It's gonna be available in— oh, actually, yes, it's being recorded. Actually, it's on my YouTube right now. So technically, um, I would go to my YouTube channel. It's actually being recorded on my YouTube right now. You can actually just watch it on YouTube. Um, Hold on. You can actually just watch it on YouTube, like, right now. You can, or right after it's over. Any other— we got 8 minutes left. All right. Scott says AI art cannot be copyrighted because it doesn't meet the human authorship requirement of US copyright law. That means that the output generated by AI image generators using text prompts is not eligible for copyright protection. Yeah, so anyone who has a copyright question, you're good to go. Do we click on publish website after? Yes. Thank you. I forgot. I was getting caught up. Yes. So click Publish website. Preparing design. And then it's there. That's your website. Go to view website. Let's see website. Scroll down. There it is. That's why in the beginning where I said make sure the category you click. Is website. Um, will a template link be available? Uh, yes, I will, I will edit the, um, I'll make sure I edit the, uh, description to put the template link. So that's why it's good to rewatch it on YouTube, because I, I'm not gonna be able to do that on LinkedIn. Um, so yes, so that's why again it's important that when it comes to the category, you click you had website checked because this is that, that published website is not available for those other ones. Um, and this is your actual, again, this is your actual URL, right? This is your URL. And what you would do is when you go on your, um, this is just some other extra sauce, uh, go on your resume, right? Oh crap. Go on your resume, you have portfolio at the top, and then just put that same link, that Canva link. That's the sauce. That's how you do it. Highly recommend also, just like how Chris had his portfolio in his featured section, y'all should have your portfolio in your featured section. Right? You can press a, uh, go to your featured section, um, add a link, add your portfolio sites. What's the site? What's my— you copy and paste your portfolio site, press add, it's gonna pop up. And then, you know, use like a— you put a thumbnail there, you know, put a thumbnail there. Uh, no, you don't need Canva Pro, um, to publish. Uh, if there is something with Canva Pro, if it says something about Canva Pro, that means that I think there might be, um, there might be an element on here that is Pro-specific, but it's gonna say what it is. But you don't, you don't have to, you don't need Canva Pro to publish. I, I definitely verified that with my students there. That's why I was saying it was important not having elements that have a Pro, because it's— then it won't publish. But you don't need to have Canva Canva Pro to publish. And again, it's only— at the end of the day, it's really, it's really a— everyone should have their own professional websites. Um, as far as a career, your professional career, you always want to have a website. So I do think that a Canva Pro for $10 a month, I think, I do think that's a good investment just in general. But no, you don't need Canva Pro to, to publish. You know, if you're interested, if you're stuck in your job search, or if you're stuck in your career, you're having trouble getting your data analyst job or whatever job that you're trying to go for, please send me a DM and I'll see what I can do to help. Loud noise. But otherwise, no one else have any questions, then I will Let y'all go. Have y'all hope y'all have a good rest of your Saturday.

Every data career channel is telling you to put your portfolio on GitHub, and today I'm going to show you why that advice is the reason hiring managers are closing your portfolio in under 10 seconds, and what I switched to instead that takes 80% less time to build, costs nothing to host, and actually gets interviews. Because a problem with your portfolio was never the projects; it was the format. And nobody told you that because everyone in this space is building portfolios. Portfolios the way developers do. You are not a developer, you're a data analyst, and the person reviewing your work is also not technical. And I only figured out what I'm about to show you because I spent months doing it the wrong way first. My early portfolios were GitHub repos, and every single update was a fight with the markdown file structures and formatting that had nothing to do with data analytics. I was spending more time maintaining the container than building the actual work inside of it. And that is when I realized that entire approach was broken. All right, so let me tell you something most people in this space never talk about, and it is the single most important thing about your portfolio. The people reviewing your portfolio, they are not data engineers. They are not scrolling through GitHub repos for fun. In most companies, the first person who sees your application is a recruiter or a hiring manager who has hundreds of other applications sitting in the same queue. They are going to spend at most maybe 30 seconds deciding whether your portfolio is worth even looking 30 seconds. Now think about what happens when they click on your GitHub link. They land on a page full of file names, random file names. Maybe they see a README if you wrote one, or the default README that has nothing in there. Maybe they see a list of randomly named folders: Project 1, Project 2, underscore, whatever. There's no visual hierarchy, there's no narrative, there's no way for a non-technical person to understand what you did, why it mattered, or what the business outcome was without clicking into files, scrolling through code, and doing cognitive work that they do not have time to do. So what do they do? They close the tab and they move on to the next candidate. And your work, which may have been excellent, never got seen. Not because it wasn't good enough, but because they literally couldn't get to it. I talk to people every single week who are in this exact situation. They have all the projects, they put in the real work, and they're not getting callbacks. Why is that? One person I know said it perfectly. You could tell me to build a portfolio, but if I don't know how to build it, you're just telling me words. She had the skills, she had the projects. What she didn't have was a format that made her work accessible to the person evaluating it. And she's not alone. I hear this constantly. Hey, I've been applying for months. I have a portfolio, but nothing's happening. And when I look at the portfolio, the first thing I see is this unorganized GitHub repo that requires technical fluency just to navigate it. That is the barrier between your work and your offer, and you built it yourself without even knowing it. So most advice in this space focuses on what projects to build, and that matters, of course. I have talked about that extensively on my channel, but almost no one talks about how to present those projects, and the how is what determines whether anyone even looks. Think about it from the hiring manager's perspective. You got 2 candidates, same level of work, Same type of projects. One of them sends a GitHub link with 6 repositories and a README that is completely blank. And the other person sends a clean visual one-pager site where every project has a title, a business question, and a visual. You don't even have to click anywhere. Everything is on it. You just have to scroll through it. There's no login, no clicking through file trees, and no password protection. Which one do you think gets reviewed? Which one do you even think gets remembered. It's not even close. And the person who built the better projects but put them in the worst format just lost to someone who may have been less skilled but was easier to evaluate. Think about your favorite supermarket. Mine is Kroger. If a new Kroger was being built in my city but the outside looked like a prison, I'm not going in there, and no one else is either, because the outside looks like a prison and it's not a prison inside. It's an actual supermarket, but how are we supposed to know that? Same thing with putting your portfolio in an unorganized GitHub. Your portfolio is a product, and just like any product, if the user experience is bad, nobody cares how good the underlying technology is. They already left. So after months of fighting with GitHub on my weekends, updating markdown files, fixing these broken image links, restructuring repo folders every single time I added a new project, I stepped back and I asked a simple question: what does a portfolio actually need to do? Not what the internet says, not what do other data people use— what does it need to do for the people reviewing it? And I landed on 3 principles that I call the 3 E's. Number 1: easy to access. The portfolio must be accessible with one click. No login, no GitHub account required, no cloning repos, no downloading anything. One URL and the person is looking at your If there's any friction between the hiring manager clicking your link and seeing your projects, you are losing to candidates who simply had portfolios that loaded instantly. Think of every extra click as a drop-off percentage. Next, easy to navigate. The portfolio must be structured so that a non-technical person, which is most people, can understand what they're looking at within 5 seconds. Clear project titles, clear section headers, a visual layout that guides the eye from project to project without requiring the reviewer to figure out how your file structure works. If someone has to wonder, where do I click to even see the actual work? You have already failed a navigation test. All right. Third, easy to consume. Every project in the portfolio must communicate 3 things without the reviewer having to open a single code file. What was the business question you answered? What did you find? And what do you recommend? If the only way to understand your project is to download Jupyter Notebook files or having to download these SQL files or having to read through 1,000 lines of code, the reviewer is not going to do it. They will close your tab and move on to the next person. Your project insight needs to be consumable in under 30 seconds, actually probably under 10 seconds per project in plain language with a visual. And a GitHub repo fails all 3 of these E's. Quick pause. Now, if you want to land your first job in data analytics, and avoid the lengthy mistakes that I did, click the link in the pinned comment where you can find the exact steps that my students use to land their job in data analytics without going back to school. All right, now let's get back to it. So what passes the 3 E's? What format is free, requires no code, and can be built and updated in a fraction of the time it takes to maintain a GitHub portfolio? That is Canva. And I know some of you reacted with, what the heck is Canva? That's a design tool. That's not a serious portfolio platform. I hear you. And I thought the same thing, but let me show you exactly why it works better than anything else I've tried. And I've tried everything. So here's why Canva passes all 3 E's. Number 1, Canva allows you to publish any design as a free website on its own URL. No hosting fees, no domain purchase, no code. You build it, you hit publish, and you get a link that opens instantly on any browser. You can take this link and add it to your LinkedIn and your resume. Easy to navigate. Canva is a visual-first tool. You drag and drop sections and you create clear headers. You can control exactly what the reviewer sees and in what exact order. There's no file trees. There's no README competing with other code files for attention. The layout is exactly what you design it to be. And easy to consume. For every project, you can create a visual card with a business question on top 3 key insights in the middle and recommendations on the bottom, and a chart or 2 with a dashboard screenshot as a visual anchor. The entire project is consumable in under 10 seconds without opening a single file of code or having to download anything. Now I know what some of you are thinking. Won't hiring managers think Canva isn't serious? Don't they want to see my GitHub to prove I can actually code? 2 things. First of all, your GitHub does not have to go away. You could absolutely still have your repos. With the full code. You can link them from within your Canva portfolio under each project. But the first touchpoint, the thing that determines whether they look further, needs to be visual, fast, and accessible. And that's going to be your Canva portfolio. GitHub can be the backup for anyone who wants to go deeper, but honestly, most won't and most don't care. The ones who do will click through. Second, the hiring managers for data analytics roles are not evaluating your Git They are evaluating whether you can find insight, explain it clearly, and make a recommendation a business can act on. A clean visual portfolio demonstrates all 3 in under 10 seconds. A GitHub repo demonstrates that you just know how to commit code. Which one do you think gets the interview? All right. So this is what I want you to do today. Go to Canva, pick any of the thousands and thousands of different website templates, put your name on the top and add the projects that you have been building in your spare time. Publish it, post it on LinkedIn, and then you can feel free to tag me so I can see it. That is a 30-minute task that will do more for your job search than the last 30 hours you spent fighting with your GitHub repos.

This is the exact same roadmap that I used to help me pivot from making $15 an hour to now making over 6 figures in data analytics. And I've also helped well over 1,000 other career changers break into data analytics. From various backgrounds, from truck driving, HR, teachers, all sorts of non-technical backgrounds to land roles at places such as Roku, Microsoft, and more. So stick around and I'm going to reveal exactly what to learn, when to learn, what tools to learn in what exact order, and also how to add consulting experience on your resume for free. On your resume for free. Okay, so Going to talk to you about the roadmap, right? The fastest data analyst roadmap for career changers. Who am I speaking to, right? If you are a burnt-out mid or veteran-level professional, you're overworked, you're underpaid, you're stuck in maybe a non-tech admin type role, or just in a career that lacks growth and on the decline, right? Or you're maybe a recently laid-off professional. Maybe you've been laid off for well over a year. Maybe you have a non-technical role or a lesser technical role and need to pivot into a more in-demand growing career, one that's you know not getting automated. Like if you're in a more technical role, for example, such as data analytics, a perfect one to transition into. Let's say you are one of the self-taught strugglers in tutorial hell right now, right? You have been trying all the YouTube Udemy boot camps and still not landing interviews. Maybe you are also not just confident, you know, in your skill set to even apply, right? So if you've done all these things and you're stuck, I'm talking to you as well. And then also, if you are an underpaid, you know, professional with a STEM degree, right? You're stuck in these low-paying jobs despite a strong academic background. I'm talking to you, right? Number one, you don't need to go back to school and get another degree, and you don't need to wait on more experience. I'm going to show you the 4-phase Elevate method that you can enact right now so that you can get a data analyst role literally in 6 months or less. Depends on really how fast you'd like to go. Okay, so phase 1, build the skills that pay, right? So 60% of employers list hard skills as one of the most important qualities among successful candidates. Okay, so hard skills This is something that we cannot ignore. This is something that you can't just skate over. You need to not just have any type of skill, you need to have the right skills. And I'm going to teach you exactly what is the exact right skills you need to focus on and hyperfocus on, and which ones are just not as important. Okay, overall we have Excel, statistics, SQL, Tableau or Power BI, data storytelling, Python, and and PowerPoint. And I'm going to go in depth on, um, exactly what you need to know on each of these, you know, at what level, um, and when you can move on to the next phase. So it's very important, um, that we, you know, it's very important that we get to a certain level. Let's say we learn Excel first. You, you need to understand certain things before you move on to the next thing, right? There's Certain things you want to understand in SQL before you move on to like the next tool, because what you don't want to do is just want to go fast or just want to get the certificate and just speed through. Okay, that type of speed you get, you're just going to pay for it at the end because you're not going to be soaking in exactly what you need. You're not going to be able to apply what you're actually learning because you're not really soaking in the information. Okay. So what to learn and in what order. So learn these skills in order. So the first step is thinking like an analyst. So this is probably going to be the least technical part. So understanding the data analyst lifecycle. So in the real world, you know, ideally you don't just start working analyzing data right away. There's a lifecycle defining the objective, right? You need to understand the business problem. Collecting the data, cleaning the data, analyzing, drawing conclusion, and then reporting your results to the stakeholder. This is that step cycle that you need to understand, and you need to understand the exact steps, you know, what tools you need to use, that questions you need to ask. But this is the entire life cycle and what we do. Some people just think, you know, being a data analyst is just crunching numbers. That is not the case, and you will completely understand by the end of. This video. Okay, key performance indicators. All right, these are indicators that we use to analyze performance, right? Create benchmarks. We use statistics and domain knowledge/business knowledge to develop KPIs when doing projects. There's quantitative, there's qualitative, there's leading strategic. These are— again, we're not just going in and just coming up with random numbers. We are measuring business processes and creating benchmarks and KPIs is a great way to do that. So that is definitely something you need to understand when it comes down to being a data analyst. Third, problem-solving techniques. So all you do as a data analyst is basically solving problems. We deal with complex problems and data, and we need to understand how to learn frameworks and structured approaches so we can break these down into manageable parts. Okay, so there are some frameworks such as DMAIC— define, measure, analyze, improve, and control. This is a Six Sigma type of thing. This is just like a good framework to get inspiration of, right? So defining the problem, knowing how to measure, right? And then you analyze, right? You don't just go in and start exploring it, right? You need to define the problem because there can be a million different directions you can go into. But when you understand the problem and define it very well, and then understand the types of KPIs to use to actually measure it, everything else would be a lot easier, right? So that's what you need to do. Another framework that's good are the 5 Whys, and this is great for root cause analysis. So again, when you get a problem, Usually when you get a problem, especially in the job, the first, you know, the first answer that you get when you ask a question usually isn't the, you know, the root cause. You got to keep asking to get more in depth. And the 5 Whys is a great, great, great, great framework that is great for root cause analysis. So this is again just great problem-solving frameworks that you should understand as a data analyst. Right? And this right here will set you apart from most people because this is usually stuff that people like to skip because maybe it's not as boring and people just wanna get into, again, analyzing data, but that's just not how things work. All right, now our very first tool, Microsoft Excel. So Microsoft Excel, what should we understand in Microsoft Excel? You should understand how to analyze data with pivot tables, right? Very easy to do, drag and drop. You know, know how to create charts with pivot tables. Should also understand, you know, lookup functions, you know, basic lookup functions, you know, VLOOKUP, XLOOKUP. Understand conditional formatting for like tables. And then also you wanna understand statistics and focus on descriptive statistics, different data types, scales of measurements, you know, measures of central tendency, mean, median, mode. I would highly suggest you learn and apply this in Excel before moving on to another tool, right? You want to just— most likely, mostly everyone comes in already being familiar with Excel, so you want to understand as much areas of analytics in a tool that you already understand before you move on to other tools that you're learning for the first time. So focusing on descriptive analytics, again, handling outliers, univariate analysis, bivariate analysis, some multivariate as well, and understanding shapes of distributions. Beyond that, if you want to go higher, I'd say probably A/B testing, but descriptive statistics. Don't worry about predictive, don't worry about that stuff, just descriptive. And then data cleaning. So understand how to handle missing data, datetime manipulation, outlier treatment, standardizing formatting. This is a— data is always going to be dirty in the real world, and you want to understand how to deal with it. So understanding how to do that in Excel and then also in Power Query. Power Query is a powerful tool already in Excel where you can also clean data, also in a more automated format or automated way. So mainly, again, understand how to analyze data with pivot tables, lookup functions, conditional formatting, understand statistics in Excel, okay, data cleaning, and also apply the same data cleaning in Power Query. Um, notice I don't really have anything else. I don't have VBA. You really don't want to be— you don't have to be like an Excel wizard these days. Um, if a job is like heavy in Excel It may not be a high, a very great higher paying job these days unless you're in like FP&A, especially how the way advanced automation is working now. There's only so much Excel you really need to understand in terms of its functionality. But this is like the core areas of Excel that you definitely want to be comfortable with. All right. Now, data storytelling and visualization. In Excel. So take away Excel for a minute, but the main thing is data visualization and data storytelling, right? So data visualization, right, if you take— if we start off with a spreadsheet, visual data visualization is just like taking— going from the spreadsheet and just making like a bar chart. But just a bar chart alone is not data storytelling. That doesn't— just a bar chart doesn't tell a story. Uh, there's elements of data storytelling elements of color theory, elements of, you know, clutter, removing clutter, etc. And you want to understand these principles very early and again, understand how to apply them in a tool that you're already familiar with, with Excel. When it comes to data storytelling, most people are not good at it. Most aspiring analysts are not good at it. Most people in the job market trying to apply for data analyst roles are simply not good at it. Why I feel like most people just don't spend enough time or they under— I think they underestimate really how valuable data storytelling is and they overestimate their ability. To be honest with you, when it comes on data storytelling, you have to both really mainly think about how is the data that I'm presenting, how is my audience, how are they getting it? Right? How are they processing the information? Which is not really anything, you know, that doesn't really encompass anything else that we're doing, right? If we're just working on functions in Excel or just working on our own in Python or whatever, we don't really have to think about that kind of stuff all the time, like what other people may think, because we're working alone. But when it comes down to presenting our analysis, we need to understand really the art and science of how our information is being digested. So information design is huge and I highly suggest getting into the weeds early again in a tool that you're familiar with in Excel because you're going to continuously apply these principles when we get to Tableau, Power BI, and slide decks and PowerPoints. All right. So the data storytelling, and visualization, I have on the screen examples of what I have my students create in my program. So I have a module for data visualization storytelling in Excel. So these are just simple things, some, some charts that I just have them make so that they can, you know, just practice, you know, certain, uh, just, just practice early on how to tell a story, right? So you want to be able to make charts similar to this. You know, how to remove clutter, you know, using color to pinpoint or direct the audience and what's important or not. Um, but that is highly suggested, right? Okay, now next one, Tableau or Power BI, business intelligence tool. Whatever one you pick, there's multiple— there's enough jobs for, for either or, but just pick one. Or at least start with one and get really good before you even think about the other. Now, a big problem when it comes down to this, you know, people learning this is that honestly they don't really have great storytelling. They don't really have great data visualization skills. I'm going to show you in the next one, in the next section here, what I'm talking about. But this is a type of level what you have on the screen. This is the type of level of dashboards you at least want to have. You know, on your portfolio. This is at least what you want to be able to do by the time you want to apply to jobs. So these are all examples of dashboards I have my students make. So the very first one is actually something that we make in our course or program, and the other 3 are examples of what my students have made as well. So again, this is— again, this is stuff that you want to be at least this type of caliber minimum before you move on to anything else. And I have a little, you know, stop here, right? So if you still make dashboards and visuals like here, like I have below, do not move on to the next step, right? So if you're making dashboards and visuals that are similar to this, for example, which many of us do, this is This is just like a rite of passage. Everyone does this when we first learn, you know, Power BI and Tableau, which is fine. But I want you to understand that there's— we still gotta level up, right? We're not ready to move on to nothing else if we're still doing this. So again, so if we're doing this, if we're making dashboards like this, this is not a dashboard. This is just a canvas of a bunch of charts. And not some of them not very good charts, right? This is like again, so let's be honest with ourselves. This is just a little check in if you're making dashboards and visuals like this. We're now moving on to the next step. There's more to learn, and don't worry. At the end, I'm going to show you some. I'm actually going to share some really good resources for you, so you can really get up to speed on your data visualization skills and data storytelling skills. But again, if your if your dat if your portfolio is is stuff like this, you're not ready yet, right? Minimum this level of dashboards and, you know, charts. Okay, so let's say we're good to go. Now we're heading on to SQL. So with SQL, you want to make sure that you are good to go with joins, unions, datetime manipulation, subqueries, CASE WHEN, window functions, aggregation, CTEs, these, you need to be comfortable with, you know, advanced, you know, intermediate and advanced SQL. SQL is a very popular tool that's used for technical assessments, more popular than Python, for example. So SQL is very, very, very, very important. So it's very important because it's most likely something you'll use on the job. And it's something that you can expect in a technical assessment, especially if you're trying to go for the, you know, 6-figure roles. You're going to need to know SQL. So you need to be comfortable with window functions, you need to be comfortable with CTEs, you need to be comfortable with CASE WHENs. You should also understand what they are, right? You should also be able to explain what does a CTE do in your own words. You should also be able to explain when it's more optimal to use a CTE, you know, versus a subquery. Right? And, and vice versa. Um, so make sure you understand that. And now once— let's say after you take a course, how do you practice? There's tons of, you know, SQL interview practice sites. Uh, I highly recommend DataLemur though, from Nick Singh. Probably one of the best ones because his questions are also a little harder, or— but it's, it's, it's very good. You don't, you don't want easy anyways, right? So his site's the best one, definitely. Practice on a site like DataLemur. Don't just take, you know, course after course after course. You definitely want to do a lot more practice, right? So again, when it comes to SQL, you want to make sure that you're good to go on these, on these concepts, because again, this is going to be a big thing when it comes down to technical assessments. You're not going to get the job if you don't know SQL. That's just plain and simple. Um, okay, next one that no one really ever talks about— slide decks, right? PowerPoint. So as data analysts, we don't just, we don't just make dashboards, we don't just make reports, we also make PowerPoint presentations. And the best way, or in, well, the best type of slide decks that I like to use as examples to teach are consulting-style slide decks. Now, everyone doesn't make slide decks well, so there's some really bad examples, but you definitely want to be able to do great slide decks. Sometimes I see people, they'll have a deck and it's just, again, it's just they just throw charts on there or they just copy and paste their Tableau or Power BI, you know, charts on there. And that's just not the case. That's not what you should be doing. You want to learn how to have an action title at the top. Subtitle, be able to add, you know, different types of context here. So again, these are— you wouldn't make this— this isn't something that would be on a Tableau dashboard, for example, but this is something that would look great in a PowerPoint presentation. So you want to be able to learn how to do this. And also a reason why is because when it comes down to, again, interviewing, especially if you want to go for the higher paying roles, Take-home assessments are also very popular, right? You get a dataset, they give you a business problem, and then you'll have to do your analysis and you'll have to create a slide deck presentation. And if you create the presentation and it looks like junk, you're not going to get invited back to present your present, right? If you create a good slide deck, they'll bring you back. You do great in presentation, you start to— you do well. But this is a very important element of skills that I honestly don't really see a lot of other people talk about, but it's definitely important that you need to know. So you definitely need to know how to create good slide decks. So action title, subtitle, one chart, one slide, one big idea. So these are just examples of some decks, some charts from online. You can see all these things online. Um, as well. But you want to be able to make decks like this, right? So a good way to practice, right, is to, you know, again have just one deck or one slide of a title, subtitle, and then a chart with some, you know, added context that, you know, more than, than you would normally do on a Power BI dashboard. But super important, okay? Now, now we get to Python, okay? We don't even think about Python until we get those other things right. Honestly, you can, you can still get a lot of roles without Python, um, and honestly, Python's not always necessarily tested on in, in interviews. That's why it's the last thing you should be thinking about, um, and it's also pretty optional. But if you want to do Python think about the data pipeline, think about automating processes, think about data cleaning and more end-to-end, right? Maybe you're acquiring data, you do a project where you're acquiring data from API or something like that, or you're starting with a bunch of CSV files, you create some functions or pipeline to clean them up, and then you, you can, you know, have something where you can have clean data for your for some visuals or for your dashboard. But think of data cleaning, automating pipelines, and automation, you know, automation, automating processes. Then you can go into like visualizations and analysis. But data cleaning, automating pipelines, automating processes, kind of end to end, almost like analytics engineering. So that's the focus with Python. Don't necessarily think, you know, visuals first. Think of the data cleaning automation. So the stuff that we spoke about in Excel, right? We— I talked about learning the data cleaning Excel, handling outliers, missing data. You want to be able to do that in Python, which is way easier than Excel. Way, way easier. By the time you get to Python, you'll already, you know, you'll already understand how to do these things with data cleaning, visualizations, analysis. So you're just learning the tool now. Right? You're already used to, you already understand, you know, how to apply, when to apply these things. You're just learning the tool, so it should go much faster. All right, so that's Python. Recommended resources. Okay, so if it's not obvious, data storytelling, data visualization is huge. It's also something, again, most people are not good at it. And honestly, it's going to take longer to really grasp. It's going to take feedback. It's going to take being exposed to just new information constantly. And it's not something that you can just watch a simple tutorial and just automatically get. There's so many rules when it comes down to data storytelling. So first thing I would start off with is Storytelling with Data. Read it once, then read it twice. It's a good— it's good short read. It doesn't take very long. Then Better Data Visualizations. This is a good reference book. It's not like an end-to-end read. This is a good book to reference whenever you're building out visualizations, your dashboards. This is something I used to have. I used to have the physical book on my desk at my very first analyst job. Whenever I wanted to, whenever I was ready to start doing charts, I'd reference this book. When I to when I wanted to start off with what information I wanted to share or what information I wanted to present. And then I went to, you know, just saw what, you know, the recommended data visualizations. Second one, Information Dashboard Design. This is also a good reference book, not necessarily like an end-to-end read. This is something that I had also a good reference book on my desk at the physical book. On my desk. And I think actually you can only get it in physical book copies now. So I have the physical copy on my desk, uh, for my very first role as well, my very first analyst role. Uh, and when I was ready to design my Tableau dashboards, I always reference this. Uh, there's 13 common mistakes in this book that goes over that. It's a must-read. So this is, this is a must-read. This is a must thing that you need to understand. Because one thing about data visualization, data storytelling, dashboard design, there's just a lot of mistakes that we all make that we don't understand that we're making, right? Without someone else coming in or getting feedback. And if we're not, you know, if we don't have a mentor, if we're not, you know, getting feedback, you know, we're kind of just running blind here. So we really have to educate ourselves. On, you know, what's— what actually works, mistakes, and what actually looks good, you know, for presentation. Another one's Lean Analytics. This is just a good general book for, um, just learning how to just analyze, uh, data and just how to measure performance for a business. So this is good for just adding some more business sense for those who are coming from very non-business-like backgrounds where they don't really got that business type of education. Big Book of Dashboards is a good reference book as well. It's literally what it says. It is a big book of dashboards. It has dashboards from various industries and it talks about, like, it breaks down the elements of it, why it's there, why it works. Also a great, great things to have. Data visualization in Excel. This is just really good too for seeing different types of visualizations in Excel. For me personally, whenever I make my PowerPoints, I always make my visuals in Excel and then I add the context with PowerPoint. So I always like to learn any sort of information on how to make my Excel visuals better for my PowerPoint slide decks. Another one, because one thing about— again, that I want to say, what you don't want to do is get caught in a cycle of taking course after course after course. So if you take one SQL course, um, you don't want to take more SQL courses. If you take, you know, one Tableau course, you want— you don't want to just keep taking more Tableau courses. So once you take a course Right, you want to be able to practice, right? And so a great way for practicing for data visualization storytelling, this book, Storytelling with Data: Let's Practice. This is like a part 2 of the Data Storytelling with Data book that I mentioned at first. It just provides a bunch of practice. That's literally all it is. And that's something that you definitely need to do if you're— again, if you're not in a group, if you're not in a course, you're not in a program, this is a way for you to create your own practice. So for example, in my program, I provide weekly practice in weekly coding challenges, SQL, and also stuff for data visualization. So if you're not in a program already, you have to create this yourself. And this is a great, great resource to do that. Okay. Lastly, okay, I want everyone to look up the Urban Institute. It's called the Urban Institute Style Guide, uh, Urban Design Institute Style Guide. Um, this is a great thing for, um, learning how to design slide deck, um, charts or slide deck presentations as well. Uh, it's great for choosing color. Uh, it's great for choosing color for when you have multiple categories. For example, if you have like 5 categories, you know, suggested color, colors, whatever. If you're not dealing with a brand. So if you're at work, you'll always have the brand coloring for that company. But when you're doing projects on your own, most of us are not very good at choosing what colors to use. But there's all sorts of color design theories or color theory, and you definitely want to use the Urban Or the Urban Institute Style Guide. Highly, highly, highly recommend. Just Google Urban Institute Style Guide. It's just it's a website that you should definitely bookmark. Okay, we have the skills, but you gotta be able to showcase the skills. So if we're coming from all sorts of various backgrounds, right? If we're a teacher, we're a banker. If we are like a sales manager. And it's not obvious that we've done analysis, we have to really put in some, just some extra work to build a brand that says, hey, we've been doing analysis all our career and we're doing it now. All right. And so a brand is how we attract high-paying data analytics roles that we deserve, whatever that means for you. Right. And having a strong personal brand in this job market is really a non-negotiable. And we talk about the three parts of your brand assets: resume, portfolio, LinkedIn. Most people only focus on their resume, right? Most people only focus on their resume. But if you do that, you're at a disadvantage, right? You're you're really you're you that's one third of what you're supposed to be doing. Portfolio and LinkedIn. And I'll say this: I posted a job on. LinkedIn. Well, I made a post. So one of the mentors in the program, his team is hiring for a data analyst position. And so I made a post to find someone who I can personally fast-track for that data analyst position. I got probably 300 responses, 300 DMs, 300 connection requests. And honestly, like 99% of the people were not qualified. One of the big things, multiple reasons, but one of the big things is people were reaching out to me who obviously came from different backgrounds, but their LinkedIn, because that's all I had to go for, I didn't have time to just look through people's resumes. Their LinkedIn did not say anything about I'm an analyst or I've been analyzing data. Nothing. Some people didn't even have analyst on their headline. So it's super important to make sure that your LinkedIn conveys again that you've been analyzing data, you've been analyzing data in your career and you're doing it now. Whether you're doing it now means portfolio projects, whether you're doing it now is also just on your job and just knowing how to write, translating your bullet points For an analyst role, but it's super important. You can't just have a LinkedIn that has nothing with analytics on there, and then you know say that you you want to apply for an analyst role. You're not going to be. You're not gonna get. You're not gonna get chosen because there's so many questions there. Okay, so we're gonna talk about each one by one. So fixing your resume. I did a live. Resume roast the other day. I highly suggest you looking it up on my channel. It's called Live Resume Roast with a Technical Recruiter. It was Pedro Zambrano, and what I did was I had a Google Form. We pretty much took in people's resumes. We you know scrubbed their you know personal information, but I also had a little poll there to see what people's challenges were. When it comes down to the job market. And when it comes down to job market, release their resume, people are submitting their resume, they're not getting callbacks, they're not getting interviews or responses, uh, even when they feel that they're qualified, right? And going back to what I just said, a lot of people feel that they're qualified for things, and unfortunately they're not. But that's a different story. But just say people feel like they're qualified and they're not getting callbacks or responses. So again, if there's a misalignment with what your actual skills are and what your resume and LinkedIn are putting out, that's, that's not the fault of, you know, the recruiter or the company. Well, you're not really conveying your skills properly. You're not conveying your experience properly. So of course you're not going to probably get a callback there. Getting ghosted, rejections with no feedback. Not getting shortlisted despite strong alignment with job descriptions, tweaking resumes for each role, experience gap, you know, lack of relevant experience or portfolio projects. And I'm going to speak about that, how to get around that in a second. You know, underemployment, no current exposure to needed tools, struggling to break in from a general or non-technical background. So again, this is for folks like that. This video is Just trouble positioning themselves. So all these types of common challenges a lot of us are dealing with in the market. And then I also ask, hey, what actions are people doing? Right? Tailoring resumes for each role, getting career advisor feedback and optimizing for ATS. Really, really high scores. I would say honestly, a lot of these ATS score, a lot of these ATS score. Websites are just trash. They're not real. So take those scores that that gives you with a grain of salt. Just revamping the resume, improving portfolio projects, taking more courses, all this stuff. So people are doing things. But what we learned, honestly, what we learned in this resume review session is that honestly, a lot of people they don't understand or they don't realize how much they're actually getting in their own way. So this is a resume. Obviously it has Batman. But a big thing that we notice is that a lot of people are not putting impactful bullets on your resume. So, for example, this one, this very first one, developed and analyzed tax forms, ensuring 100% accuracy. In tax calculation, like 100% accuracy, that's an impact, right? But you need to understand the difference between impact and tasks. So task of, hey, I researched tax laws to make updates, or I implemented this for whatever. You need to have the result of what that task was. You need to have the result of what that That action was. Because other than that, it's just a task. If your bullet doesn't have an impact of the result, it's not, it's not really— then it's just, it's not, it's not really, it's not that. You, you, every bullet needs to have some sort of impact, um, impact or result of it, right? So task versus impact, it's not the same thing, right? Many of us do plenty of different tasks all day, at work, but very few— there's very few that actually provide some sort of impact or whatever the impact was, right? 50% increase in this, 30% improvement in efficiency. That has to be there in every bullet. So that's number one. We saw that most resumes just didn't have that, right? And when we talk about also like a professional summary, It's good to have like a very short 2-4 liner professional summary, nothing too big, but especially if we're coming from, you know, non-technical backgrounds. If you're a teacher, it's good to have like a good, almost like an elevator pitch, right? Not a big paragraph, but, you know, something that maybe takes about 2 to 3 lines on the top. And another big thing too is you want to make sure your skills are at the top as well. A lot of people don't put skills at the the top, sometimes at the bottom of your resume. Another thing that I wanted to mention, especially for those of us who have plenty of experience, you know, they'll put projects at the top before their experience. You definitely want to put your experience first before projects. Your experience matters more. When you put your project top, it kind of makes you look a little more junior. So that's definitely expected for like college kids without experience. But when you have a bunch of experience, you want to put your experience at the top before your projects. Because again, a big thing is you don't want to position yourself as a junior or entry level when you have a bunch of experience. When you do that, it's going to be very difficult for you to stand out in this market. Very lastly, this is actually the best resume of the night that we had. This is actually a resume for someone in my program. She had, you know, her headers. She had her LinkedIn link at the top. She had her information. She had a link to her portfolio, which we'll discuss in a sec. And she had her skills and certifications at the top. And she had her bullets that had all of them had some sort of impact, right? 200+ new hires, you know, 3% late fees, 400% improving efficiency. All that's That has to be there, right? Every bullet needs to have that, and then you want to highlight those as well. Again, you want to make your resume easy to clearly showcase your value, and your value is all about the results. We don't no one cares about the tasks. Okay, so portfolio. You need to have portfolio. You need to have evidence, right? When I looked and saw, or when I was responding to people. for that role that I put out, or, you know, I was personally going to, I was going to personally fast-track this person for, or the prospective person for the role. Everyone was saying the same thing. Hey, I just graduated the school. I can do Excel, Python, SQL. Everyone was saying that. I wanted to see proof, right? If I, if you went to a school that I have no association with, I don't know the caliber of that program. I don't have time to just sift through all your resumes, right? So I would rather see people's portfolios. No one, barely anyone had a portfolio, which is unfortunate, right? You actually, it's actually good to lead with a portfolio when you're actually networking on LinkedIn because it's so much easier to get your information and you can add more stuff that you can't add on your on your resume. So for example, this is a portfolio from my— one of my students, Caitlin. She transitioned from a teacher to a finance analyst and she has a portfolio. She has her projects. Again, you can't put this stuff on your resume. So if I'm seeing this, right, what she did was she posted her portfolio on LinkedIn and she ended up— that ended up leading to a recruiter getting in her inbox. And within a week she had an offer, right? It's good that I can easily see your competence in these tools that you claim to have, right? So that just decreases the questions, that decreases the risk because now I can see the work that you're doing. Most people don't have this, right? Again, she posted this at the time, excited to share that she's going to start work as a finance analyst. So again, her transition out of the classroom. So that's just a clear example of why having a portfolio is so, so important, right? It shows proof. It shows evidence, right? You saying that you went to this school, you went to this boot camp, you telling me that you have these skills, that's not evidence. Everyone has that. And especially nowadays where Everyone ChatGPTs their resumes. It's the resume is honestly not as it's not that great of an indicator anymore of you know how well you actually are. So that's why a portfolio is important, and most people don't have one, right? So that is how you that is how you stand out. This is another one of my students, Ibrahim. I mean, look at that. You you go on here. You see his skills and expertise. You can click to view his resume. But this is a great way to lead people on whenever you're networking or you want to draw attention to you. This is way better to lead with a portfolio website with your projects that people can see visually that they cannot get on their resume. Right. So this is your evidence. Your portfolio is your evidence that of what you claim on your resume, right? And so other examples is another one of my students. This was made in Canva. You know, you can get all her information that you can get on the resume as well. And then another one of my students that you can make this in Notion. So you don't have to have a fancy website. I also have a video on how to make a no-code portfolio on my channel. Highly suggest you go through there, but you can create one in Canva and 10 minutes, and I show exactly how to do that. Okay, now how do we create amazing portfolio projects that will actually land us offers? So once, right, in the first phase, right, you're making projects, you're learning, but how do we develop amazing portfolio projects? Remember, there's 2 types of projects. There's projects for learning, right? That was mainly in phase 1. And then there's projects for showcasing, right? Projects that showcase on the portfolio. You know, by this time we'd probably have done— I don't know, you could probably have done like 30 projects, 20 projects, you know, just as you're learning Excel, Tableau, Power BI, SQL, etc. But not everything needs to be put on a portfolio. Your portfolio could be, you know, maybe like 3, 4 projects. You don't really need that much. You don't really need that many. And you want to make amazing portfolio projects. So what are the characteristics of an amazing portfolio project and how do you do it? So first up, you want to identify high-value problems in, say, your target industries. So let's say you're coming from a healthcare background, you're coming from a financial, you're a banker. Whatever industry that you're in and you're targeting, what are the key high-value problems in that industry, right? And what could be improved? You can research industry blogs, Reddit, LinkedIn posts. You can also— and this is also a great thing you can use AI for, right? You can research this on Perplexity or ChatGPT. And just make sure everything has an associated source so you can verify it. So you understand these problems. So let's say if it's marketing and it's converting, online conversion, whatever it is, but get the high value problems, write them down. Okay? And once you write them down, next step is that when we're creating projects, you're going to create projects around the high value problems that we just looked up, right? And so you find real world datasets based on the problem that you just looked up. And here, you know, you can Google, but here you can just again use AI. You can look them up on Perplexity or GPT and say, hey, I want to do data analytics projects on, you know, this type of subject or this type of problem in this industry. You know, where can I find data for that? And it'll provide you. That's a good way to start. And maybe Kaggle has some, but you just want to make sure you're creating projects around these high-value problems. It's just not anything. So, and put them on your portfolio, right? So this is, this is a, this is a good, this is a good way to create your own amazing portfolio projects. I do this for my students in my program, but again, you can just do this yourself. Doing this way. Okay, so last step in this phase, LinkedIn. So some— a lot of people too who responded to me, again, 2 big core problems on their LinkedIn page. I didn't see analyst on anything on their webpage, on their LinkedIn. Nothing said analyst, right? So if you're coming from a different background, coming from a different industry or different career, You have to translate. You have to translate. You have to translate all that towards relevant analyst roles because it just comes off. It looks like it's just a mismatch, right? So if you're in insurance, that's great, but I don't see nothing with analyst. Nothing is displayed that you've been doing analysis and you're doing it now. So those are two things that you need to display again on your brand assets. You've been doing analysis and you're doing it now. So these are examples from my students. So Ryan is actually a college student. And again, big thing, actually a big thing too. So when it comes down to the time people spend on a LinkedIn profile, the average is 15 seconds and half of that is spent on the banner. So look at that banner, right? So I have the first section that people first see when they look on your LinkedIn page. And don't just have some, you know, random banner. Have something that talks about you. It can be very simple. So Ryan Jacobs, machine learning scientist. I build automated machine learning systems that optimize business processes. Most college students just say, you know, Economics graduate or economics major at XYZ College. It's just very boring, just very vanilla. You have to do things to stand out, and there's very simple things to do it. Caitlin, same thing. Name, data analyst, 3 core tools that she's good at: data analyst, storyteller, transforming numbers into insights, using data to improve processes. Most people just don't really have much going on with their headline. And again, it's just different. It pops. You know, professional photo, right? You need to have a professional photo. Most people do not have a professional photo. And also smile. I've also noticed that a lot of people are not smiling in their professional photo. You need to have a friendly demeanor. Again, from data to real-world impact. This is another one of my students, right? Professional photo, important. Data science, machine learning, analytics engineer, transforming raw data, actual insights for curiosity. And precision. This is great, right? And most people aren't— this is eye-catching. Another one of my students, right? Larita, senior people analyst at Roku. Like, this is the type of professional photo most— I'm telling you, most people here, most of— most people, most job seekers get this wrong. Um, I got some people that DM'd me where they didn't even have a professional— they didn't even have a photo. It was just gray. So you also want to make sure you check your your settings so that your profile photo can be visible to everybody. Like, I'm not going to respond to someone without a photo. Or some people didn't even have a banner, or some people had nothing really much in their headline. This really stands out. You already jumped to the 1%, right? And this is Hakim, another one of my students. Again, really great banner. Like I said, 15 seconds. 15 seconds is the average time people spend on a LinkedIn profile, and half of that is on the banner. And what does it say? Analytics engineer, right? Data strategy. That's what it gives off, right? It just does. Just hits you in the face. That's what it should do. Okay, now when it comes on to your LinkedIn headline, here is a simple template you should do. I help, you know, type of company. You can say, I help businesses solve specific problem by using your top skill or edge, right? This is a good framework, something similar to actually what Ryan did. I build automated machine learning systems that optimize business processes. Very simple, very simple than just anything else. Don't ever put aspiring analyst, never do that. So just follow that framework and then you can have, you know, tools, you know, for search engine optimization as far as LinkedIn. Okay, but that's, that's mainly phase 2, you know, we're still, we still got some more time going on. All right, phase 3. This is something that says optional but highly recommended. Ended. So most people say, oh, I don't have experience. Well, you'll create your own experience. You create your own experience with freelancing for local nonprofits. You position yourself as an analytics consultant, use your portfolio with your best polished projects as proof of work. This will be volunteer freelancing. The idea isn't about money, it's about adding professional analytics experience to your resume, right? You take their Excel sheet— like, these are just local nonprofits, so it's not going to be big fancy setups. Like, you just take their Excel sheets of data, do analysis, and deliver a report in a consulting slide deck presentation, like I mentioned earlier, with recommendations in 30 days or less. So this is what you do. You reach out to your local nonprofits and you ask them, hey, can Can I do some free freelancing analytics work? In return, you're able to add that to your resume and then you deliver what I just said in 30 days or less. Now, yes, you can potentially work longer there. That's fine. But the idea is you want to present or provide them value in a relatively short time first before anything. What you don't want to do is you're not going to You know, you're not going to, you know, kind of abuse the situation and just, you know, have them on your resume and not do anything. You want to provide some sort of value in 30 days or less. Could you do longer? That's fine. But again, the idea isn't about money because you're going to make more money when you land your job in analytics, your full-time job. This is just something you can do on the weekend, you know, a couple hours a week. And provide value. And in return, again, you get that experience on your resume. So by this time, right, by this time you've been building your skills. You should be keeping them fresh. You have a brand, right? Rather than just do— continue to just do simple toy datasets, why not work on actual, you know, actual data with actual businesses? So local nonprofits, This is you know what you know big universities do with their graduate students all the time, so you get that real-world experience, and then you also have experience that you can talk to about in the interview when you start applying. So again, win-win. This is something again this will set you apart, and this just negates the I don't have experience, you know. I don't have experience excuse when it comes down to my program. I provide this for my students, so I provide a consulting project that they can do. But this is what you can do to do it yourself, right? Reach out to local nonprofits. All right, phase 4. We are here. Phase 4, crafting your story. So 57% of employers say A candidate's personal pitch makes a candidate stand out during the hiring process. Okay, so most common reasons for rejection: lack of preparation, insufficient technical knowledge. So remember we talked about SQL, vague answers, not a cultural fit, lack of energy or enthusiasm, not a fit for working environment. The last thing you want to do is position yourself as entry level. So the worst, like we don't want to be able to have all the skills, have all this experience. And then we get to the interview and we don't close the deal. The interview is where we close the deal, right? But if we are not having a great story, we're not gonna get— you're not gonna get the offer. As simple as that. Um, so you want to be able to learn how to craft high-impact stories, right? You want to write down using the STAR format, right? You want to write down, um, you know, your top You know, experiences that you've had in your past. Be able to translate those again to relevant analytics experience. Write them down. Really think of your unique, you know, position, your unique value proposition. You know, as a professional, what can you bring? What value do you bring to an organization day one? Right? And everyone should have their own elevator pitch. Right? Everyone should have their own elevator pitch. If I asked you, hey, why should I hire you for this role? You should, you should in 30 seconds or less have— be able to tell me why in 30 seconds or less. If you don't have that off the top of your head, then you need to work on your story. You don't— you need to work on your story. This isn't really something that, you know, you necessarily, you know, watch a video or anything on. You just sit down and really think about what you bring to the table, what you bring to an organization, what value do you bring as a professional. And just think back on the high impact type of experiences that you had in the past. That is crafting your story, and that's how you finish the deal with the interview. So looking back, I know that was a lot, but this is a 4-phase method, and what you don't want to do is skip steps. Don't ever skip steps, right? If you If you don't, you know, if you try to just speed through phase 1, you're not really grasping the skills. And let's say you build a brand that's getting you interviews, right? Let's say you build a great resume, but your skills aren't very good. You're going to be exposed in the interview and you're not going to get the role, right? Let's say you have great skills and you don't do what I tell you to do for your brand. You don't want to— you don't do what I tell you to do for your resume. and your LinkedIn, then you're not going to get interviews. So again, so there's— you have to do each of these, right? Let's say you do step 1, you have all the skills, you have the best skills in the world, you have the best brand in the world, and you get to the interview and you're just not very good at communication. You know, you're not good at communication, you don't do the STAR method, you're not going to get the role. So So at every instance, if there's one thing that you don't do well, it can't get canceled. Like, it breaks everything. You're not going to get the offer. But this is the 4-phase method that, again, has worked for me, has worked for my students, has worked for others. So this is not just some system that is just random. I guarantee you, if you do this exactly, you're going to land your role. Within the exact time frame. No need to go back to school, you know, no need to waste more money on multiple courses. This is all you need. And if you are also interested in getting more help or assistance, uh, please reach out to me. I'm going to have a link in the description so that you can see if we can work together so that I can help you land your dream role in data analytics. There are 8 interview cheat codes that data analysts are using to get hired this year, and most people have never even heard of a single one. I've seen these exact moves work for myself, my students, and for people inside the entire data community for the last 5+ years. These aren't shortcuts. They're not gimmicks. They're cheat codes that the 1% of the 1% are quietly doing while everyone else is grinding LeetCode and refreshing their inbox. All right. So first, the cheat code that closes the interview before it even starts. And I call this one the unsolicited value move. It's where most candidates would never even think to go. Most candidates will spend 0 hours on preparation. They'll skim the job description, maybe Google the company name, and then they walk in hoping their resume does all the talking. Before the interview, you need to do real homework on the company, not surface-level homework, real homework. You pull public data on them, App Store reviews, comparative pricing, recent press releases, Glassdoor reviews from the inside, and you build a quick one-pager, a simple summary, a simple dashboard, a single chart, something that you can reference in the conversation or share if the moment opens up for it. I'll give you a real example. I once interviewed for a role at the Dollar General. Around that time, they had a recent merger with Family Dollar, but most candidates would've overlooked that. I read up on it, and in the interview, I asked specific questions about that merger and how it was affecting the data team in that particular warehouse, if at all. The whole tone of the room changed because suddenly I wasn't just a candidate hoping for a job. I was someone who had already started thinking like their employee. And here's the key. You keep it humble. You don't just walk in claiming that you know their business better than they do. You frame it casually.

Hey, I was curious about your recent product launch, so I pulled some public data to see what the initial reaction looked like. That one sentence shifts the entire interview and it moves you from being interviewed to being consulted. Hey, real quick, if the first one has already got you rethinking how you've been showing up to interviews, do me a favor and hit the like button. It tells YouTube to push this to other career changers who are about to walk into the wrong kind of interview. So let's keep going. Okay, so now that you've shifted the tone in the room, the next cheat code is what I call the anchor cheat. This is a live portfolio walkthrough. Everyone else talks about portfolios to get the interview, but almost no one talks about how to use it inside the interview. And that is the gap. Chris Voss spent his entire career as the FBI's lead hostage negotiator. And in his book, Never Split the Difference, he wrote, the person across the table is never the problem. Unresolved issue is. So your portfolio walkthrough works the same way. Don't walk them through your tools. Walk them through the problem that you solved. That's what they're actually buying. Here's the move. So you build a clean 3 to 5 page slide deck of your best project or a simple one pager. Nothing fancy, nothing busy, nothing overdesigned. And during the final round, the one in front of the hiring manager or the VP, you screen share it and walk through it live, real time, out loud, the way you would present it to a real stakeholder. You start with the problem, then the data approach, then the business impact, and land it with one clean line. I found X, I recommended Y, and it saved the company Z. This is the move only the 1% of the 1% are making. Because here's what hiring managers actually want to see. They don't just want to know you can write code. They want to know that you can stand in front of a room and communicate what the data actually means. A live deck proves that in 5 minutes. A GitHub link does I even had a student who recently did exactly this and landed a 6-figure role by showcasing a portfolio he made in my program. I've seen it work multiple times. It does work. Okay. And once you've been walking through that deck, you need a cheat code framework that actually shows you how to talk through it. Everyone uses the STAR method, situation, task, action, result, and it's been standard for years and it's fine. But for technical questions or project walkthroughs, there's something even better. And it's called the PACE method. P for problem, A for approach, C for code or analysis, and E for execution and impact. The reason PACE works better than the STAR method for analysts is that it frontloads the business problem before any of the technical stuff. Harvard Business Review published a piece with a line that stuck with me forever. The best job candidates are the best storytellers. PACE is how you tell the right story. Not a tools dump, not a recipe, a story with a problem, a decision and the result. Because when an interviewer asks, what did you actually do in that project? The worst answer is a list of tools or what other people did. I use Python, I use SQL, I use Tableau, I clean the data, blah, blah, blah. That's just a tools dump. That's not an analysis. That's a recipe. PACE forces you to answer a different question. Whose time, whose money, and which department did your analysis save? That's the answer that gets you hired. All right. So the next cheat code is a secret weapon for career changers. And honestly, even if you're early in your career, you can steal this. It's weaponizing your domain experience. Here's a mistake almost every career changer makes. They hide their past. They downplay their years they spent in retail, in teaching, in healthcare, in anything really. And then they show up to interviews positioning themselves as entry-level when they are absolutely not entry-level. If you were a teacher for 10 years, you should be targeting edtech companies. If you spent years in retail, e-commerce is your goldmine. If you worked in nursing, healthcare analytics is sitting there waiting for you. David Epstein wrote a book called Range, and his core argument was this: the most effective people are those who have developed a range of skills and experiences and who can apply them into new contexts. So the career changer who just learned SQL, that's not a liability. That is a range hire. The kind of person most data teams are desperately missing right now. And if you're scared about AI replacing you, let's talk about what AI cannot do. AI cannot replicate 10 years of standing on a warehouse floor, knowing how supply chain actually moves and how bottlenecks actually affect the bottom line. AI cannot replicate years of running a classroom and knowing the variables that actually affect the student success. The context is what makes you irreplaceable. So stop walking into interviews saying, hey, I just finished a bootcamp. I just finished my master's. That's vanilla. That's fluff. That is what 99% of the candidates sound like. And that's exactly why they all blend together. Lead with what you know that the data team currently doesn't. That's where your leverage is. All right, quick interruption because I know some of you are watching this thinking, okay, the strategy makes sense, but I still don't have the actual data skills to back this up. That's exactly what I cover in my program. If you ever want to work with me to land your first job in data analytics, click the link in the description. All right, so let's keep going. Okay, so you've done the pre-interview work, you've done the walkthrough, you've talked through your project with pace, you've led with your domain. Now this cheat code, we're actually getting to the part of the interview most candidates actually completely waste. The questions section. When the interviewer asks, do you have any questions for me? Most people pull out some generic like, what's the company culture like? What's a typical day look like? Dead end, dead air, zero impact. I'm telling you, the interviewer has already heard those same questions nonstop. So you're not blending in here. Now, you don't have to wait until the final 5 minutes, but you can weave strategic diagnostic questions throughout the entire interview. And the questions you ask are designed to surface the team's actual data problems. Not to check boxes. Questions like, how often do stakeholders actually use the dashboards your team uses? What's the biggest bottleneck in your current data pipeline right now? Where does a team spend most time on work that doesn't get used? These questions do something powerful. They shift the entire dynamic. You stop being a candidate getting interrogated and you start becoming a consultant diagnosing a problem, and the interviewer will feel that shift. And so can the panel sitting next to them. Okay, so now here's the one that experienced hiring managers are quietly testing for, even if they don't say it out loud. The cheat code is what I call the messy data reality check. When they ask about a last project, you don't show them a clean, polished, everything-worked-perfectly version of something that you did in Kaggle. You highlight the part where the data was broken. Missing values, contradicting fields, duplicates that shouldn't exist, requirements that changed 2 days before the deadline, a stakeholder who sent you a CSV that didn't match the data warehouse. In the Checklist Manifesto, there's a great quote: the problem is rarely a lack of knowledge, it's a failure to apply it correctly under pressure. So hiring managers aren't testing whether you know the tools here, they're not testing if you know messy data, they're testing whether you can think when the data is broken and the deadline is real. The messy story proves that you can, because the thing is, Kaggle datasets are perfectly clean while real-world data is famously disastrous, and the experienced hiring managers know the difference. So when your interview answers are too polished, too smooth, too perfect, it's actually a red flag because it tells them you've only worked on tutorial data. And if you're thinking right now, but Kedisha, I only have Kaggle projects and everyone says use Kaggle. What messy data am I supposed to be talking about? Here's the move. And I say this all the time in my videos, and I'm going to continue to say this. Once you have all the skills, do a 30-day volunteer analytics consulting gig. Find a local nonprofit, a local small business, whatever it is, tell them you want to clean up their data and build them a dashboard for free. And in 30 days, you will have experience with real messy, broken, and contradicting data to talk about. And then you'll have a real story because here's what you have to internalize. Interviews aren't just about what you put on your resume or in your portfolio. Interviews are about the stories that you tell and the people who lose offers are not the people who lack skills. They're the people who can't tell good stories about the skills that they have. Now the interview is done. You virtually shake hands or in person if you're there. You log off, you exhale. Most candidates think that the work is over, but it's not. The next cheat code is what I call the value-add follow-up. Within 24 hours of the interview, you send a thank you email and a LinkedIn DM, but not a generic one. Yours has something inside of it. A mini deliverable, a one-page document, a quick visual, an expanded answer to a question you probably fumbled on, or structured recommendation tied to something that came up in the conversation. There's a famous Maya Angelou quote that said, I've learned that people will forget what you said, people will forget what you did, but people will never forget how you made them feel. A generic thank you email makes them feel like you sent the same one to 12 other companies, but a mini deliverable makes them feel like you're already on the team. That's the difference. All right, quick pause. Now, if you want to land your first job in data analytics and avoid the lengthy mistakes that I did, click the link in the pinned comment where you can find the exact steps that my students use to land their job in data analytics without going back to school. All right, now let's get back to it. There was a hiring manager who even told this story when a candidate sent a follow-up note where they casually mentioned, hey, by the way, this kind of analysis saved my team $40,000 a quarter. That candidate was hired on the spot. One sentence in a follow-up note mattered more than the entire interview because the thing about interviews that's understandable Your nerves may talk in the room, but your work can talk in the follow-up, and work is always louder. All right. And now we are at the final cheat code, the final boss, if you will, the one that turns the last 5 minutes of the interview into a job offer. And I call it the consultant close. One of the best lines in Never Split the Difference is the best negotiators aren't the ones with the best arguments. They're the ones who've done the most homework. So the consultant close is that homework made visible. Most candidates will walk into an interview trying to answer every question. The sharpest candidates walk in already diagnosing the problem. This requires real homework before the interview. Do as much research as you can and look for the cracks. Then in the final 5 minutes of the interview, instead of asking a generic question, you can make a statement. So based on what you describe about your data reporting backlog, I've solved this exact problem before, and here's how I would approach it in the first 90 days. That single move transforms the close of of the interview. It stops from being a polite formality and it becomes a compelling business case to hire you. Because at that point, they're not deciding whether to hire you, they're deciding when you can start. All right, so here's a bonus cheat code, and this one happens before the interview even shows up on your calendar properly. I call it the LinkedIn Pre-Connect. When a recruiter sends you the interview confirmation, you usually get the names of the people you'll be meeting with. Most candidates will just glance at the names and close the email, but that's a wasted opportunity. Here's a playbook. You go to LinkedIn, you find each interviewer, you send a LinkedIn connection with a warm connection note. Something like, hey name, I'm Kedisha, looking forward to our conversation this Thursday. Excited to meet you. No pitch, no ask, just a warm signal that you saw them as a human before you walked in. And this is the part most people miss. You actually study their LinkedIn profiles, what they did before the role, how long they've been at the company, what kind of projects that they post about, if they do, what does their team structure look like if you can see it, because all of that becomes ammunition For the interview. When they ask you, do you have any questions? You already have them already. Hey, I noticed you came into this role from a product analytics background. How has that shaped how the team approaches reporting today? That kind of question is impossible to fake, and it only comes from someone who did the homework, and it lands every single time. All right, so let's wrap this up. I want you to start using these cheat codes in the rest of your interviews. Show up to these interviews most prepared, most strategic, and the most consultative candidate in the room. That is a game that gets you hired today. Now, which one of these 8 or 9 are you going to try first?