Give Me 23 Minutes and I'll Build Your Data Career Roadmap From Scratch

The reason you're 8 months into learning data and still don't have a job isn't because you're not smart enough. It's because you never had a real roadmap. You probably just had a random tutorial playlist, and I can prove it in the next few minutes by building the real thing for you from a blank screen block. Here's what no one's telling you. The internet is full of a thousand voices all screaming different things. Learn Python first. No, learn SQL first. Just network. And everyone ate a good one. So you're not stuck because you're lazy. You're just stuck because you're drowning in conflicting advice with no order to any of it. And when you follow that noise, you'll spend another year busy, broke, and still no job. So the next few minutes, I'm going to help you build a complete start to finish data career roadmap on this blank screen. The exact skills, the exact order, and the timeline to land a job in the next 4 to 6 months. And the last step is one that nobody includes. It's going to be the difference between I'm studying and I'm hired. So don't click off before we get there. Okay, so when it comes down to pivoting in analytics, and I've seen this over the, you know, past 5+ years, there's really 3 main things that's really made everyone's path much harder and just way more slower than it needs to be. Number one, shiny object syndrome, right? Some people, right, they, they see something online and they want to be an analyst. Then they see something online the next week and then they switch paths and want to be a data scientist. Then they see something, they want to be like some AI engineer or whatever it is, like, you You gotta stop hopping between different paths, different tools, and different courses. You just have to stick with one thing. The next one, never deciding on a target and really just not having a plan, right? I just want to do data. I want to pivot into data. That's not a plan, right? You will never be successful. You will never make the pivot if you do not stick with one thing. Number 3, they only build but never position, right? So these are people who, you know, they have all the skills in the world, right? When it comes down to positioning themselves, when it comes down to showing up with their brand and showing up with their resume, whatever it is, communication, they just get passed over. So these are the main, the biggest 3 reasons why I see people who they'll start the pivot and just never actually make it through or they're never successful. If you find yourself in any of these 3, stick it out. I'm going to show you exactly how to break out of these. Now, if you're new here, my name is Kedisha, self-taught data scientist turned instructor and data career coach. And I've helped thousands of other career changers break into analytics without going back to school. And if you ever want to work with me to land your first role in data, click the link in the description. All right, so let's get started. So when it comes down to pivoting into data analytics, making that career change, there's really 2 main forces that's going to determine your success. That is going to be positioning and discoverability. So positioning is all about, again, how you position yourself in front of decision makers, you know, hiring managers, recruiters. Recruiters, right? That is going to be your resume, your LinkedIn profile, everything. How do you show up, right? How do you also package your skills, your experience into your elevator pitch, into your value proposition? All of that stuff is about positioning, right? If you imagine, let's say we're in the store, right? We're looking for chips, pizza, whatever it is. There's stuff on the, you know, eye level in the supermarket. There's stuff way above at the top, and then there's stuff on the bottom, you know, most likely the top selling items or whatever it is going to be usually eye level, not stuff you're going to have to at the top where no way you could reach it or whatever it is, but positioning matters, right? Discoverability. So today in this job market, you need to be discoverable online. LinkedIn, right? The way you show up on LinkedIn. Recruiters live on LinkedIn all day long. What does your LinkedIn give off? Is it set up in a way That is attracting DMs to your profile when you sleep, basically. So positioning and discoverability, we're going to talk about those things. Now, before going any further, there's a huge misconception on really the job market right now, right? So in reality, there's a huge gap between the job market in terms of the applicants and the companies posting the jobs. Most of you are going to be the applicants. And you're like, man, there's so much competition. There's not enough roles, whatever it is. But the companies, right? There's so much data out there that companies are actually having a tough time finding enough skilled workers for their job applications. Now that sounds crazy. You're like, hmm, what does that mean? Right now in the job market, there's a huge quality issue. There's a huge quantity of applicants, but thanks to, you know, ChatGPT and AI, everyone can pretty much fake their resume and things like that. There's not a lot of Quality of job applicants. So again, while there may be this disillusion that oh there's too much competition, etc., there's actually not that much competition, right? There's a huge skill gap of folks applying for jobs and the companies posting jobs. I'm going to show you exactly how you can pretty much close that gap so you can be on the other side. All right, quick pause. Now if you want to land your first job in data analytics and avoid the lengthy mistakes that I did, click the link in the pinned comment where you can find the. Exact steps that my students use to land their job in data analytics without going back to school. All right, now let's get back to it. All right, so your roadmap from scratch, right? So there's basically going to be 3 steps plus a bonus, right? We're going to talk about foundational skills, really how to pick your target role and the core skills it demands. Then we're going to talk about a gap analysis. I'm going to talk you through my Elevate method. And then again, if you stick to the bonus, I'm going to show you how to basically triple your income. In the first five years of data compared to everyone else. Okay, so number one, step one is about identifying the foundational skill. So remember, too many people—they're not sure whether they want to be an analyst, a data scientist, or an engineer. They're always flipping through. If you're a career changer and you're trying to pivot into analytics, the most common entry point is going to be a data analyst. That's also going to be the most highest probable. Entry point is going to be a data analyst, right? Why? Because pretty much so many jobs, you're basically an analyst, right? Every professional is an analyst. Every professional is a data analyst, right? Tools don't make the analyst. Unlike data scientists that, you know, involves much higher amounts of maths and machine learning and coding and data engineering. Those are not typical entry-level jobs, right? It's very easy to look up. The salaries and say, oh wow, well, you make more money being an engineer than a scientist. But in reality, the data analyst is going to be your common entry point. And so that's what you definitely need to focus on. And again, it's going to be the best if you have a non-technical background, right? Don't be swayed by too many folks who go to like these data scientists boot camps, et cetera. So many people go to these boot camps or get a master's in business analytics or data science degree and just think that they can take their non-technical background, get this degree or get this certificate, and then automatically Exactly, they can step into ML engineer roles or etc. Too often I've seen people spend so much money on those paths only to realize that there's just too much of a gap in their experience, and then they feel like they have to settle for an analyst. But that's just not how it works. You know, it's it is what it is. Data scientist, data engineer more so of not necessarily entry level, but best if you specialize after you're an analyst, right? All right now. When it comes down to it, once you've identified, hey, whether you're going to be an analyst, a data scientist, a data engineer, you want to do a skill gap analysis, right? So let's talk about data analysts, right? Because that's going to be the most common entry point if you're a career changer into data. You look at what are the skills that are necessary, right? Excel, SQL, Tableau, data storytelling, whatever it is, Python. Then you give an audit. Audit yourself. Rate yourself. 0, you never used it before. You know, 1, you've been exposed to it. You know how to use it a little bit. 3, you know, you can do, say, like a project with that skill on your own. And 4, you know, you can teach people that skill, right? Audit yourself, grade yourself on it. And at the end, you'll be able to see, you know, what skills you're missing, right? So you want to give a good, honest skill gap analysis so that you don't go off and waste time, you know, taking courses, et cetera, that you probably don't need, whatever. You can kind of focus on exactly what you need to Do first. Okay, so once you identify that skill gap analysis, then you can take focused course. You can do focused courses on exactly what you're missing. So if you realize that your skill gap is very low on SQL, for example, but very high on Excel, you know, maybe do a very quick project in Excel, but you want to spend more time during your week on SQL, for example. Maybe you realize that your SQL, you know, you're exposed to it a little bit, but you're not at the level where you can teach someone CTEs or window functions. Rather than spend a whole bunch of time on maybe, you know, the skills that you're already well equipped with, you can focus your limited time, right? We all have a finite amount of time. Focus on just, you know, those core things of the skills that you're missing basically, or that you're lacking. Of all of these though, Excel, SQL, Power BI, Tableau, you want to have some sort of artifact or evidence of these skills, and we're going to talk about this in the next one. All right, so we're going to step into the Elevate Method. So after we've done our first couple steps, which is identifying our target role, whether it's data analyst, data engineer, data scientist, then we're going to, you know, identify the skills necessary for that role. We're going to— we did our gap analysis to see of the skills that we have, what's our skill level, what's our, you know, level of ability. And then we see the gap of the skills that we don't have, the skills that we need. All right. And we've taken courses. We've done the coursework to train up, you know, what we need. Then we're going to get into this ELEVATE method here. Okay. So I talked about those skills, right? We need to have some sort of artifact or proof and evidence that we know Excel, that we know SQL, that we know Tableau, et cetera. And we want to build high value projects about those using those tools. Now, most people, they'll go on say Kaggle or whatever, or they'll go on YouTube and just do some, you know, follow along tutorial and then slap that portfolio project on their portfolio. Do not do that, right? Today, what you want to do is build your portfolio projects around high value problems in your target domain. So number one, there's going to be 3 different types of high value problems that you'll, you'll look at. There's data centric, right? So what are these common challenges that data teams face? It can be, you know, no process or standardization, very weak data governance, you know, dashboards that no one uses. There's end industry-centric high-value problems, right? Like supply chain inventory optimization, patient outcomes in healthcare, you know, conversion and churn issues in retail. And then there's organizational-centric problems, right? These are problems within a company. So if you want to target, say, like a FAANG company, those public companies that have, you know, 10-K reports that you can look at, you can look at, you know, issues and problems basically about that company. For most of us who want to pretty much build a high value portfolio and high value projects around a high value problem, industry centric is going to be our go-to, right? So have your target or define a target industry or 2, ideally whatever industry you're currently in or pivoting from, because you already have that domain expertise. And what you'll do is I've already made some YouTube videos around this. You can use AI, ideally, you know, a Manus. Something with an agentic capability, you can have it source real datasets and create an actual project for you around high-value problems in your target industry, right? So I did a project or did a video on doing that with supply chain, way better than Kaggle. Most people are going to just say, let me go to Kaggle and get some, you know, whatever is on there. Avoid Kaggle. The thing about Kaggle is nowadays anyone can just upload anything from What you want to do is you don't want squeaky clean data. You want something very specific. You want something that has like multiple tables, all of that. And you want it real and you want it very focused on high value problems in your specific domain. Let AI do the legwork. And you want to build your whole portfolio project on, you know, inventory optimization, supply chain, or whatever it is. Now, again, creating your evidence. So once you've done those projects, right? And again, you don't need 10. You just need about 3 to 5 strong ones. If you're going to do them like what I just did, just 3 of them is completely fine. You need to package the best ones in a portfolio. I am a big fan of one-pagers, no-code one-pagers. Canva is my go-to. And your portfolio needs to do 4 things. It needs to be easy accessible, right? Just a simple, you know, URL that you can link on your LinkedIn, that you can link on your resume. What most people don't realize, they will link something that, you know, needs a password or you need to log into something and If no one can see it, then no one can access it. So easily accessible, easy to navigate. I like no-code one-pagers where you can just scroll down or you can just see everything. No need to click around or no need to do anything like that. Worst case scenario is people using unorganized GitHub repos. I would avoid GitHub repos altogether. You don't want anything where people have to like look around or not really sure where to find things. And it needs to be visually appealing, right? This kind of goes without saying. The more charts and visuals with very, very good storytelling, the better. And then easy to consume, right? Use a 10-second rule. Can someone grasp what your project is about, you know, in 10 seconds? For every project, right? Don't just paste, you know, a bunch of code, 20 lines of code. No one wants to necessarily see that. It's like a snippet of code, right? You can use carbon.sh, but it needs to be very visually appealing with visuals. So you can use consulting style principles for your, you know, charts and things like that. Again, quality over quantity. At this point, right, we're in step 2 of our ELEVATE method. We got to build our brand. So again, it doesn't matter if we are the best, you know, we have the most SQL skills, we have the most Excel, we're the best of the best. If we don't know how to position ourselves in a way that we're discoverable, none of that matters. Okay. None of it. I know we've been in situations where we've seen people with less talent, half the talent that we do, getting the promotions, getting the jobs. Okay. It really comes down to the brand, right? Your brand has 3 assets. Most people only focus on one, right? It's your portfolio, your resume, and your LinkedIn. All 3, they all work together. Nowadays, I'm telling you, anyone I've seen, there's job applications that will ask you, hey, do you have a URL or a portfolio? They will ask you for your LinkedIn. Even if they don't ask you for your LinkedIn, I guarantee you the recruiter or hiring manager will look you up on LinkedIn. So all 3 work together. Most people just focus on their resume, but you can't just focus on your resume and have like a Nothing on your LinkedIn, et cetera. You have to have all 3 work for you. So I want you to do also a 20-minute brand exercise. And this is also going to play into step 3, right? I want you to think about, because it's one thing to just say, oh, your LinkedIn, your resume, things like that. But you have to know what your brand is about. What problems do you solve? Take a step and think about what 3 to 5 problems do you solve? What stories from your past do you have? Where you've solved those problems, and what are 5 character traits that you want the hiring manager to know about you? You want to have a story for each of those character traits. Okay. And you got to make sure you tie it all back to your target role. If you take some time to do this, I just guarantee you it's going to be way 20 times easier to craft your resume, craft your LinkedIn, craft your portfolio. And also it's going to lead to our next step. And that's crafting your story. The previous step I told you to create those stories, really think about them because too many people will get the interview and just bomb the interviews, right? Because they don't know how to tell their stories well. They will position themselves as entry level. They think that their stories aren't that interesting, or they don't spend enough time to really craft their stories, right? Think about your transferable skills, right? Think about your soft skills and really think about and craft your high impact stories to tell in your interviews. You can tell the same stories in your interviews. It's not like these employers and all these jobs are, there's a database to say, oh, you know, you said that last week in another company. Now, understand too, once you get to the interview, that company or that hiring manager, they are completely fine with your experience. Very often we've gotten, you know, rejections, rejection emails after an interview. They're saying that, okay, well, we were looking for someone with more experience. That means you did not have a great story to tell, basically. That means you failed this, you know, part 3 of the Elevate Method. Because again, they can see your experience on your resume. But if you get there and again, you downplay your stories, you don't have the high impact stories. You don't flesh them out enough to where you are the star. You don't say we did this, we did that. You are the star and you really focus on the results. You're not going to do very well. You're going to bomb the interview. So again, crafting your story is a big thing. This is something that I highly, highly recommend taking the time, really thinking back on your career path. Where are your high impact stories that you can tell in basically any interview? You definitely always want to flesh those out. Okay. So that's pretty much step by step on how to craft your own data career path. Now I'm going to put some money in your pocket, right? I'm going to show you basically the secrets that accelerated my career. Basically that put over $350K in my pocket in W-2 money in the first few years of being an analyst. I want to show you exactly how to grow your career faster than anyone else and earn more than anyone else in your first few years. And it's all about being, learning how to be a great Everyone's a consultant. You're either an internal consultant or an external consultant, i.e., McKinsey, BCG, PwC, etc. But you're an internal consultant, right? Your stakeholders, you're consulting them, but you have to learn the art of consulting. It's not about just being good at skills. It's not about just creating pivot tables. It's not about creating dashboards. So there's always going to be 100 things to do in a business. And I highly, highly encourage you to always try to find the high value projects. There's always 100 things to do in the business, and you always want to find, again, the high value stuff. And the way to do it is basically a bottleneck analysis. One thing I like to call it is the lever method. And the lever method is literally all about, again, defining what is the bottleneck, how to attack it, how to exploit it, and move forward. Again, there's 100 things you can do in a business, but if you identify the bottleneck, And you adjust that lever, you focus your concentration on that, you're going to have the most effect on the bottom line than any other thing. It's just like the 80/20 rule or the Pareto principle, right? You always want to identify what is that 20% that affects the 80%. And again, if you think about a business, right? And this is just an example. A business can be lead flow. It can be conversion. It could be operations. It could be retention. Again, there's so many things, so many levers, but you want to identify how do I identify couple levers that I can pull out of the 100 that will affect the most on the bottom line. And this is a version of it, right? You want to map the flow. So map the, you know, process, do a process map, identify what that constraint is, exploit that lever, create systems and create, you know, new methods and methodologies, create systems for the team to continuously, again, you don't ever want to go backwards and then just elevate and repeat. Here's a cheat code, right? So again, identifying the bottlenecks. Once you think of like, hey, what is the constraint of this team? What is the constraint of this business? What is the constraint of the process? And then you want to focus your energy on there. That is the ultimate cheat code. So in your interviews, I want you to start using this in your next interview, right? When they say, hey, do you have any questions? I want you to ask them, right? If you look at your team right now, what's the single biggest constraint holding back your throughput? And if I joined, what's the one lever you'd like me to adjust first to move the bottom line? That's going to change the whole interview. Right? That is a high leverage question, and that instantly shifts you from just a regular candidate that asks the same vanilla questions to someone that's thinking like a consultant. It signals that consultant mindset. It signals that you want to work on the high value projects that really moves the needle. It's going to make you memorable because no one else is going to do it. Okay. So once you get your first job, right, this is going to be your pretty much 30, 60, 90 day playbook. So in your first month, right, you know, you're getting used to everything, whatever, you know, this may even take 60 days, but you definitely want to audit, right? You want to be able to create process maps or process flows of everything that you're learning, right? You're learning, you're going to be learning the business processes and you want to be able to literally draw, do create process maps of everything that you're learning. And once you do that, you will visually see, be able to identify the bottlenecks. It's going to be visual, right? Once you're mapping everything out visually, and then you can present that to leadership and you can propose, you know, targets. And then, you know, based on what they say, you can pretty much execute on all those things, right? That's going to, you're going to surpass everyone and just really pretty much position yourself as a consultant, but also someone who is not just waiting just to take tickets. The money in data is not in the first job. The money is going to be in the promotion and beyond that. This is the career leverage ladder. The higher you climb, the more impact. And the more impact, the highest leverage and value. And the more higher leverage and value you have, the more money you can make. You can literally build these skills that I see. Like, you can literally become like an internal consultant and have that strategic impact at your first role. Now, the salary may not necessarily follow that, right? Because it's your first job. You're not going to say, what's the promotions when it comes down to The amount you can make, you know, you can be at, you know, that first role for, let's say, 12 months or 18 months or whatever it is. And you can literally move up this ladder. Your salary may not, and most likely will not reflect that because, you know, when it comes down to just any role, when it comes down to promotion, what's usually the yearly promotion amount? What's just like, you know, 5% or things like that. The real money is going to be taking what you've done and learned and going to another company. Right? That's going to be where the money is. I had a student who his first job was like $75K. He literally was there for maybe 18 months. He did all, he rose up the ladder. He stayed there a little too long if you ask me. His second job or promotion in his career ladder, he doubled his income, right? But you definitely want to be moving up this ladder and thinking about it, right? High leverage projects, high value projects, consulting. You always want to think like a consultant. And the money will follow, right? Don't just be someone that executes tasks. That's not where the money is going to be. Okay. Again, so this is your data career roadmap. Identify your target role. Identify the skills that's needed for the role. Do your skill gap analysis. And then you create your curriculum and your, your own curriculum, your personalized, like courses or whatever it is around that skill gap analysis. And then you do the Elevate Method and then you create more money in your pocket focusing on being a great consultant. And that's going to be how you can pretty much 3x your income in the first few years faster than everyone else. Okay. So listen, I gave you pretty much everything you need to build your own roadmap. Now go build it. Don't go watch another tutorial. Maybe watch the video that's, I'm going to have here that shows you how to use AI to build a high value project. But other than that, go build these skills and put a comment on your number one takeaway. And I'll see you in the next one.