The FASTEST Data Analyst Roadmap for Career Changers in 2026 [FREE COURSE]

Everyone keeps saying that you need to go back to school or spend $10,000 on a boot camp in order to break into data analytics. Well, that's dead wrong. And in this video, I'm going to reveal the most fastest and direct roadmap to become a data analyst without wasting months and money on the wrong things. And I know this works because 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 four-phase elevate method that you can enact right now so that you can get a data analyst role literally in six months or less. Depends on really how fast you'd like to go. Okay, so phase one: build the skills that pay. Right? So sixty percent of employers list hard skills as one of the most important quality among successful candidates. Okay, so hard skills. This is something that we can. Cannot ignore. This is something that, uh, 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 PowerPoint. And I'm going to go in depth on exactly what you need to know on each of these, you know, at what level and when you can move on to the next phase. So it's very important that we, you know, it's very important that we get to a certain level. Let's say we learn Excel first. You need to understand certain things before you move on to the next thing, right? There's certain things you wanna understanding 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 first step is thinking like an analyst. So this is probably gonna 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 lifecycle 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 gotta keep asking to get more in depth. And the 5 Whys is a great, a 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? Um, very easy to do, drag and drop. You know, know how to create charts with pivot tables. You should also understand, you know, lookup functions, you know, basic lookup functions, you know, VLOOKUP, XLOOKUP. Understanding conditional formatting for like tables. And then also you want to 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 wanna 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 wanna 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 gonna be dirty in the real world and you wanna 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. 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. 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. 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 as 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— they— I think they underestimate, you know, really how valuable data storytelling is, um, 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, like, what are, 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, 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 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 charts that I just have them make so that they can just practice certain, just practice early on how to tell a story, right? So you wanna be able to make charts similar to this, how to remove clutter, using color to pinpoint or direct the audience in what's important or not. 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 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 the type of level what you have on the screen. This is the type of level of dashboards you at least want to have 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 that 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, 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 not moving on to the next step. There's more to learn. And don't worry, at the end I'm gonna show you some, I'm actually gonna 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 portfolio is stuff like this, you're not ready yet, right? Minimum this level of dashboards and 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. 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 ones. 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 versus a subquery, right? And vice versa. 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 SQL interview practice sites. I highly recommend DataLemur though from Nick Singh, probably one of the best ones because his questions are also a little harder, but it's very good. 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 course after course after course. You definitely wanna do a lot more practice. All right, so again, when it comes to SQL, you want to make sure that you're good to go on these, uh, on these concepts, uh, because again, this is going to be a big thing when it comes down to technical assessments, uh, as you're, 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. All right, PowerPoints. So as data analysts, we don't just make dashboards, we don't just make reports, we also make PowerPoint presentations. And the best way, or 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 wanna 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, 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, you know, action title, subtitle, one chart, you know, one slide, one big idea. So these are just examples of some decks, some charts from online. You can see all these things online 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 interviews. That's why it's the last thing you should be thinking about. 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 due to 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 can have something where you can have clean data for some visuals or for your dashboard. But think of data cleaning, automating pipelines and automation, automating processes. Then you can go into visualizations and then 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 and automation. So the stuff that we— I 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. Um, by, by the time you get to Python, you'll already you'll already understand how to do these things with data cleaning, visualizations, analysis. So you're just learning a tool now, right? You're already used to, you already understand how to apply, when to apply these things. You're just learning a 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, you know, feedback. It's going to take being exposed to just new information constantly. And it's not something that you can just, you know, watch, you know, 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 a 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 referenced this book. 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. I had the physical book on my desk, and I think actually you can only get it in physical book copies now. So I had the physical copy on my desk for my very first role as well, my very first analyst role. And when I was ready to design my Tableau dashboards, I always referenced this. There's 13 common mistakes in this book. that goes over that, that'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, 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, 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 is 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, you know, very like non-business-like backgrounds, right, where they don't really got that business type of education. Big Book of Dashboards is 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 you know, the elements of it, you know, why it's there, why it works. Also a great, great thing 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, if 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 learning how to design slide deck charts or slide deck presentations as well. It's great for choosing color. 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, so that's phase 1. All right, that's phase 1. I hope that was good to go. So with phase 1, the idea is that now we have the skills. Right. But 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 when you talk about the 3 parts of your brand assets— resume, portfolio, LinkedIn— most people only focus on their resume. Right? Most people only focus on the 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, 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 want to apply for an analyst role. You're not going to be— you're not going to get chosen because there's so many questions there. Okay, so we're going to talk about each one by one. So fixing your resume. I did a live resume roast the other day. I highly suggest 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 scrubbed their 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, or at least their resume, people are submitting their resume. They're not getting callbacks. They're not getting interviews or responses. 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 gonna probably get a callback. Um, they're getting ghosted, rejections with no feedback, um, 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. 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 asked, hey, what actions are people doing? Right? Tailoring resumes for each role, getting career advisor feedback and optimizing for ATS. Really high scores. I would say honestly, a lot of these ATS score websites are just trash. They're not real. So take those scores that they give 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 noticed is that a lot of people are not Putting impactful bullets on your resume. So for example, this one, this very first one, you know, 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, you know, implemented Um, this, uh, for whatever. Like, you need to have the result of what that task was. You need to have the result of what 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. 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. 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 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, very, 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. Oh, 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 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 a 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 the the 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 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 of what you claim on your resume. Right. And so other examples is another one of my students. This was made in Canva. 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 in like 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? 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 to 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? How do you, 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 conversions, 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 analyst 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 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 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 cuz it just comes off. It, 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 2 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 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 economics major at XYZ College. 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 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. Already jumped to the 1%, right? And this is Hakim, another one of my students. Again, really great banner. Like I said, 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 than just anything else. Don't ever put aspiring analyst. Never do that. So just follow that framework and then you can have tools for search engine optimization as far as LinkedIn. Okay, but that's mainly phase 2. We still got some more time going on. All right, phase 3. This is something that says optional but highly recommended. 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 a volunteer freelancing. The idea isn't about money, it's about adding professional analytics experience to your resume, right? You take your— their Excel sheet. Like, these are just local nonprofits, so it's not going to be big fancy like 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 recommended— 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, you're able to add that to your resume and then you deliver, 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 present, provide them value in a relatively short time first. Before anything, what you don't want to do is you're not going to kind of abuse the situation and just 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, a couple of 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're 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 work 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 going to get— you're not going to get the offer. As simple as that. 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 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 know, 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— you don't do what I tell you to do for your resume. in 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're not good at communication. You don't do the STAR method. You're not going to get the role. 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.