If I Had 90 Days to Get a Data Analyst Job, I'd Do This

Most data analyst roadmap videos teach you everything you could possibly learn, but this one is different because if you only had 90 days to get hired, you don't need to learn everything. You just need to learn the right things in the right order and ignore the rest. I've helped thousands of other career changers break into data analytics, truck drivers, teachers, HR professionals, people with absolutely zero technical background. And yes, the ones who got hired the fastest are not the ones who knew the most. They're the ones who focus on what actually matters. So in this video, I'm going to show you exactly what I prioritize, what I would skip, and the move at the end that gets most people hired with zero formal experience. So let's go. Now, if this is your first time here, I'm Kedisha, and 6 years ago I was delivering pizzas and Amazon packages for $8 an hour. Today I'm running a community of over 80,000 current and aspiring data professionals, and I've helped thousands of other career changers break into data analytics without going back to school and wasting money on courses that just don't work. I watch this career field transform in real time. My only, only goal is to help you cut through all the noise and endless information so you can get exactly where you want to be in the least amount of time. And no, I would not start collecting certifications. There's a whole bunch of them you can list off. And no, I wouldn't spend months learning Python either, despite how tempting it actually is. And no, I wouldn't go back to school either, because I've seen too many people waste too much time doing all of that while other people are getting hired faster by focusing on the right skills first. All right, so quick note before I get into this, this is not a 6-month roadmap. A normal roadmap would teach you everything that you could learn, but this plan is different. And this is what I would do if my time was very limited and every week I had to move closer to getting hired. So here's how the next 90 days will be broken down into. All right, so the goal of the first 30 days is not mastery. The goal is to become useful enough to solve beginner-level business problems And start building proof. Most beginners try to learn all the advanced stuff upfront, but that is a mistake. At first, you should only focus on the skills employers actually test for and ignore everything else. All right, first up, SQL. If you want to get a data analyst job fast, SQL is non-negotiable. And that's exactly why I have it as the first one. First, focus on the foundations, SELECT, GROUP BY, WHERE, ORDER BY. Then you want to move on to joins, aggregates, CTEs, window functions, and case statements. These are non-negotiable before moving on to anything else. The second skill you should focus on is Excel. Many companies run on Excel, and it's the fastest place to learn analytical thinking. You want to focus on pivot tables, lookups, basic data cleaning, conditional formatting, Power Query, and basic descriptive statistics. The third skill I would focus on is basic visualization and dashboarding. Now, don't go too deep here because you can fall into a rabbit hole very quickly and never be able to recover time that you lose from that. The goal is not to be a master of Tableau or Power BI. The goal is to understand how to turn messy information into clear business insights. So again, don't touch Python in the first 30 days, despite what you may hear online, because Python can be useful later on. But for your 90-day job search sprint, SQL, Excel, and communication is more urgent for most entry-level data analyst roles than Python is. Python is a day 60+ skill, not a day 1 skill. So by the end of the first 30 days, you should be able to clean a messy dataset, write basic to intermediate SQL queries, Create simple charts and dashboards, and explain what the data means in plain English. All right, real quick, if you're already at day 1 of your own 90-day sprint, drop a comment below and tell me which skill you're starting with. I read every single one. And if you want to be around other people running the same sprint, I'm running a free live training. The link is in the description. It's completely free. All right, let's continue. So the goal of days 31 to 60 is to stop looking like someone who is just learning data analytics and start looking like someone who can do the work. Skills alone do not get people hired. Proof is what gets people hired. And this is where most beginners fail because they keep learning and learning and learning instead of building evidence. So build 1 or 2 strong portfolio projects, not 5 weak ones. We've all done these before, and I personally have done these, and honestly, they just don't work. Everyone else has seen them 1,000 times. The Netflix dashboards, the COVID dashboards, the Titanic datasets. These are all what I call projects for learning. You do them for skill development, but you don't do them for skill showcasing. So what should you actually build? And what projects are worth showcasing on portfolios? Well, ones that answer simple business questions in plain English, like why are customers churning? Which products are actually driving profit? Where is revenue leaking? Which marketing channel is performing the best? And how can a business reduce operational waste? Okay, so what should each project actually look like? Let me give you a checklist. Every project, and I mean every one, needs a clear business question at the top. Not analyzing customer data. That's not a question. Why are our customers leaving in the first 60 days? That is a real question. Then you need a messy dataset that you can clean in SQL if you'd like, or Excel, and then a dashboard visualization with a short written insight and a recommendation. It's optional, and I mean optional, but I do highly recommend a short consulting style slide deck if you really want to take it to the next level. Now by this stage, day 31 to day 60, you should also be packaging your proof because the projects themselves is only half of it. The other half is making sure a hiring manager or recruiter can actually find it, open it, and understand it within a minute. So here's what I'd focus on. Your resume bullets should be based off of impact, not task. Nobody cares if you just cleaned a dataset. They care that you identified a $250,000 revenue leak. By analyzing customer churn patterns. Same work, but completely different impact. And your LinkedIn headline also should clearly say data analyst, not aspiring data analyst, not data enthusiast. I hate that word, by the way. Data analyst. You become the role that you are. Remember, you dress for the job that you want. Your portfolio needs to also be easy to open and easy to understand. One click from your LinkedIn. No password walls, nothing to download, no nested GitHub repos, just clean, scrollable, and obvious that even a 5-year-old can read it. And your featured projects should show business thinking, not just dashboards and visuals or just code decisions. The recruiter should be able to look at your work and immediately think this person sees the business behind the data. All right, so day 61 to 75, and this is the most fun part in my opinion. This is the part of the sprint that solves the single most biggest problem everyone runs into when transitioning into analytics. The one thing that keeps people stuck for years. How do I get experience if no one will hire me without experience? If you've ever hit that wall, and I know you have because everyone has, even I've experienced this, but don't worry because I'm going to show you a way through it. If nobody is giving you the experience, you create it on your own. I want you to actually hear that. So I'm going to repeat it because it changes everything of how you approach the next 15 days. If no one is going to give you the experience, You create it on your own. All right, so how do you create your own experience? Here's a play. Reach out to local small businesses, local nonprofits, creators, coaches, small e-commerce sites, local organizations, anyone with data and not enough skills or someone to look at it, and you offer them a simple 30-day analytics project for free. Here's a message that I would send, and feel free to steal this word for word. Hey name, I'm a local data analyst consultant, and I would love to help you understand any part of your business data for free. I can look at your sales, audience, customer data or anything that is proven to be a big blocker in operations. Would you be open to a quick conversation about what will be most useful for you? That's it. That is a message. Short, specific, and straight to the point. And it works because most small businesses are sitting on data that they don't have time to analyze, and you're solving that for them for free. But here's where most people are also shooting themselves in the foot. They get the yes, and then they try to do too much. Way too soon. Don't do that. Keep this project simple. I want you to focus on 5 things. One problem, one dataset, one dashboard, and one short recommendation deck. And make sure at the end of it you get a testimonial, a case study, or some sort of contact that you can use for employment verification. That's the whole project. And if you try to give them a 47-tab dashboard with 12 metrics that they didn't even ask for, you're going to spend 3 weeks on it and never finish. So keep it simple. Now here's why this is so much more valuable than everything else that you'll do in this entire sprint. When you finish this project, you won't just have a portfolio piece. You will have real experience, actual tangible experience, and that's completely different conversation in the interview. Specifically, what you walk away with is this: a real experience, a case study, a testimonial, and a story you can tell in your interviews, and obviously more confidence in yourself. And that confidence is the part most people undervalue because the difference between someone who interviews well and someone who freezes up usually is not the answers. It's whether they actually believe they belong in the room. This project is what's going to help you get there. So I want you to write this down, put it somewhere where you can see it all the time. You do not need to wait for a company to give you permission to get experience. Because for all the time I waited, I thought I needed someone to hire me, train me, or hand me everything. No one was coming to save me, so I had to get it on my own. And many of my community members who have gotten hired lately has also done the same thing. So by day 75, here is where you should be. And I want you to be honest with yourself when you check this, because no one is going to keep you accountable but yourself. One of 3 things should be true. One real-world analytics project in progress, one completed volunteer or consulting project, and active outreach conversations. If none of these are true by day 75, that's your signal that you are avoiding the hardest part of the sprint. Which is also the part that gets you hired. So don't skip it and don't push it to month 4. This is the real play. Okay, now we're at day 76, the final stretch, and I need you to make a mental shift here. Up until now, you've been building skills, proof, experience, but these last 15 days are different. These 15 days are all about converting all of that work into actual job offers. At this point, you should not be casually applying. You should be running a focused job search system. And those 2 words, focused system are doing a lot of work in that sense. So let me show you what I mean. Here's what your day looks like during those 15 days. Every single day, you should be moving through the same checklist. You're applying to targeted roles, not 50, not 100, but targeted roles. You're sending outreach messages, real ones to real people. You're connecting with recruiters and you're reaching out to people already working in data roles at companies you'd want to work at. You're posting or sharing your portfolio on LinkedIn. Making sure your network can actually see what you've been building. You're practicing SQL interview questions and you're rehearsing your project explanations out loud because how you say it matters just as much as what you say. So that's their daily routine. It's all about consistency over intensity because the people who get hired fastest are not the ones who are grinding 12 hours a day and then disappear for the week. They're the ones who show up every single day for as little as even a couple hours or even 30 minutes and let the system do the work. Now I have to warn you about something else, because this is a mistake I watch a lot of people make over and over again. Don't just apply to hundreds and hundreds of jobs. I know it feels productive, and I know it feels like at least I'm doing something, but that's exactly how people burn out and end up broken at month 4 with nothing to show for it. Hundreds of applications and zero interviews and feeling like the market is closed. The market is not closed. Your strategy is just broken. And here's what you should do instead. Target roles that match exactly where you are right now, not where you want to be in like 4 years. Tailor your resume bullets to the actual role. Use your portfolio projects as proof that you can do the work. Reach out to a real person at the company before and after you apply and track everything in a simple spreadsheet so you can actually see what's working. 20 targeted applications will outperform 400 spray and pray applications every single time because targeted applications get responses and responses are what we need. All right, so let's talk about what happens when those targeted applications start landing you interviews because They will. Your prep needs to focus on 6 things. The first one being SQL practice, because almost every entry-level analyst interview includes a SQL component. Practice it cold on a whiteboard or a shared screen until you can solve intermediate problems without panicking. The next one is explaining your projects clearly out loud to a real person if possible. Walk them through the business question, your approach, what you found, and what you recommend. The third is behavioral story. Go ahead and pick 4 to 5 real moments from your past work and shape them into structured answers using the STAR method: Situation, Task, Action, Result. The next one is almost always present, the tell me about yourself part. Interviewers will ask this every single time, so you should always have a 30-second elevator pitch ready that connects your past, your transition, and to what you want to do next. A critical part of the interview is why data analytics? Interviewers want to know if you've actually thought about it. Don't just say, I love data. Say something specific about the kinds of problems that you actually want to solve. And finally, how you solve business problems, because at the end of the day, that is why they're hiring you. So your portfolio gets you noticed, but your ability to explain your thinking is what gets you hired. Every single career changer I've watched land an analyst job had the same thing in common. They can talk about their work, not just show it, but actually talk about it. The why behind the choice. The what if behind the result, and here's what I would do differently next time behind the deck. That's the skill, and the next 2 weeks are where you build it. So by day 90, where should we be? You gotta be honest with yourself on this one because this is the checkpoint that tells you whether the sprint worked or not. A focused job search system running, applications sent, outreach messages sent, an interview prep routine actually in place, not just an idea, portfolio projects that you can confidently walk through, and a real volunteer analytics experience that you can actually point as proof. If you have all 6 of those on day 90, you are not just ready. You are already in motion. The interviews will be coming soon. And when they do, you're going to be prepared to convert them. That is the entire game. Now, before I close this out, quick bonus. And I think this actually is the most valuable part of the whole video. So stay with me. The things that you ignore in a 90-day sprint are just as important as the things that you do. Maybe even more important because every hour that you spend on the wrong thing is an hour that you did not spend on something that actually moves you closer to being hired. So here's what I would actively ignore. Learning Python first. Save it for 60 days plus. SQL and Excel always comes first. No collecting endless certifications. You can pick one, finish it, and just move on. Trying to master every single tool. You just need one and go deep on it. Building too many weak projects. One strong project beats 10 weak ones every single time. Obsessing over complex portfolio design. I always recommend simple, no-code, one-pager sites like you can make on Canva. Applying before you have any proof. It's not that you're not good enough, you're just not ready yet, and that's the difference. And then spending months watching tutorials without building anything. The tutorial loop is real. We've all been there, including me, but building things is the only way that gets you out of it. Okay, so this isn't about learning everything. It's about becoming hireable as fast as possible, and by focusing on the highest leverage actions, SQL, Excel proof, data storytelling, real experience, and a focused job search. That is it. And that is basically the whole sprint in 90 days. Most people who are stuck right now are not stuck because they can't learn. They are stuck because they are trying to learn everything at once, and then they end up not being able to finish anything. So do not be that person. So if I only had 90 days, I would not try to become the most technical beginner. I would try to become the most clearest, the most prepared, and the easiest to trust candidate in the pile. And if you want help running this exact sprint with someone in your corner the entire way, click the link in the description where you can apply to work with me and my team. All right, so now let's put this all into action. I'm going to walk you through exactly how to build a portfolio project that's actually getting my students hired in this market right now. Not generic, real ones. I'll see you in the next video.