The Only Data Analyst Roadmap You Need for 2026

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, is 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 interviews. And then I'm going to talk about the 4th 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 to 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 assets, think about retention. So retaining employees, retaining customers, assets such as if you have trucks, properties, etc. 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 risking our profit margins? All KPIs, whether you're in supply chain, retail, wherever you are, ideally any KPIs, any metrics are really going to fall into wherever the 3 Rs of business is going to be, right? Managing resources, managing revenue, and managing risk. This is why 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 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 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 analog lifecycle, analytics lifecycle, understanding KPIs. The next rollover into 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 pretty much an update of that is the downward trend pretty much of problem solve. 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, to stand out with data storytelling and viz. 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 1, 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 an article where MIT did a study and it proved that AI is making us delusional. You know, 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 there. You take it and 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, 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 to fit in the metric. And I said, okay, let's call. And I picked up the phone and I dialed the 1-800 number and called customer service and we just waited in silence for them.

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 that 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 of 4 for like, oh, we don't want to do problem solving or whatever, blah, blah, blah. This is where 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 ask 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, 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 1 or 2 that you really like and just like, you know, put them on a sticky note somewhere and that's something that you go to every single time you work on a project. That's week 1. Week 1, you ain't touching Excel. Excel or nothing. Not touching the tools. All about just learning about the data analytics lifecycle. It's all about problem solving. At end of week 1, you should find, you know, 1 to 2 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, I have 1 to 3, 1 to 5 in your back pocket. So you can constantly practice. The idea about these, right, these sound good, like DMAIC and 5 Whys, but the skill set comes through repetition, right? Not just like reading it one time, watching a video of it one time, and then 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 gonna be all about statistics and Excel, right? So Excel is a fundamental tool of analytics. 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, 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, 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. 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 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 gonna 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 probably going to move on from doing data cleaning Excel most likely, but it's a great way to learn how handle nulls, datetime, missing values, inconsistent formats, treating outliers. All these things you want to 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 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 better storytelling. The thing is, it's better to condense that learning curve as fast as possible. Learning to remove clutter, learning 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 couple of 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 practice sets in there. So you can actually get both books and then 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, you know, 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, they've— I've seen their portfolios, and then I asked them if they've 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 would say low level, but like stuck in level 1 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. And 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 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 gonna 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, 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 that. 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 can honestly— you can spend about 2 to 3 weeks, 4 weeks, whatever it is. But 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 ease of 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. 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 to do them. And one of the deliverables is a slide deck presentation. I've 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 4 consulting because that's like a very stereotypical industry of them making like these types of decks. So, you know, you can just look again, look up Boston Consulting Group by far has probably the best style, consistent style. So you can literally look at like Boston Consulting Group slide decks, learn about the pyramid principle. Please do not treat PowerPoint as a place to just copy paste your charts from Tableau or paste your charts from your EDA. It's just not— that's just not cutting it, right? Week 8. This is where we're 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 isn'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 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 3. 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 10, week 10, you can definitely spend more time on developing your portfolio. Portfolio. Let's talk about the anatomy of a winning portfolio. As you can see here, project title, your tech stack, you know, your key insights. And then you want to have screenshots of like kind of 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 of 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 three 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 do you think? I'm gonna 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 asking 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. Yeah, I mean, this is, this is up in the air. You know, there are certain places that I wouldn't be a culture— I wasn't a culture fit at. 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 trust in your ability to deliver. Remember, hiring is 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, like problem solving is like leg day under pressure. Right? Under pressure being a job interview or 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 depended 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 or 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 analytics 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 could be up in the air sometimes. Not getting a job can be a blessing, to be honest, because not every place is a great fit. Not having the energy or enthusiasm, right? If you go showing up to the interview, you know, 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 a 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 my 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. And so 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 projects, 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 skill set in a smaller amount of time, right? Because it's focused time and, you know, your brain develops dopamine when you actually finish something. Okay. So That really completes like the 12-week process. Now, an additional bonus, right? What's next? One of the biggest things that 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 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 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. This completes the 12-week roadmap. And if you're ever interested in working with me to help land your first role in data analytics, Click the link in the description and the pinned comment, and we'll see if we can work together.