235: How I Would Become a Data Analyst in 2027 (if I had to start over again)

Here's the exact five-step roadmap that I would use to become a data analyst from scratch if I had to start all over again in twenty twenty-seven. Now, I'm a very lazy person, and I'm very impatient, so this method, the SPN method, is designed to get the most results with the least amount of effort. Step one, and this might sound really counterintuitive here, but I'd actually stop focusing on learning data analytics skills. And don't get me wrong, the data analytics skills are really important. You need to learn them. But there's actually a minimum threshold that you need to have learned to be qualified for some sort of an entry-level job. And learning additional tools or getting better at a certain tool is not probably what's holding you back from landing your first data job. And how do you know that it's not holding you back? Well, until you've actually failed multiple technical interviews, most hiring managers and most recruiters, they can't actually gauge how skilled you are at SQL or Excel or something like that. So what is holding you back? Well, we'll get to that in steps two through five. But for now, know these two things. Number one, that you do not have to know every data analyst tool on planet Earth to learn a data job. And number two, you don't have to have mastered any data tool to have landed a job. And memorize that, tattoo it on your forehead, whatever you need to do to remember those two things but you do need some tools, you need some skills, and that's actually the S part of the SPN method. So what tools should you focus on, and how good at each tool should you become? Well, remember, I'm lazy here, and I want to focus on the tools that are the easiest to learn and the most in demand. And those tools are, in exactly this order, Excel, Tableau, and SQL. And you can remember that through this acronym I created called Every Turtle Swims. If you want to swap out Power BI for Tableau, that's a hundred percent okay. I just think Tableau is slightly easier to get started with. If you do make that swap, the acronym becomes Every Penguin Swims. So how skilled you need to be with those three different tools? In Excel, you should be able to do normal things like formulas, sorting, filtering, and pivot tables and Xlookups. In SQL, you should know select, where, from, group by, all the little operators, you know, to do the different wheres, and up to having. And in a BI tool, you should feel comfortable building a bar chart, a line chart, a scatter chart, as well as making KPIs that's all you really need to get started with. If you're learning Python or R or AWS or any sort of cloud or anything else before you're landing your first data job, I would put all of those on the shelf for right now, and the only exception would really be learning how to use AI to analyze data. Right now, eighty percent of data job listings still don't mention AI, but AI is becoming more and more important. So I still learn all the fundamentals, but I'd also be able to learn how to analyze data with tools like Copilot or Claude as well. So every turtle swims plus some AI. And the fact is, there's probably someone out there right now who is landing a data job over you, and it's not because they're more skilled, it's not because they're more talented, it's not because they know more tools. It's because they're doing steps two through five. So let's actually get into those now. Step number two is breaking the cycle of doom. Cause let's say that I focus on every turtle swims, and I learn Excel, I learn Tableau, and I learn SQL to a decent level. I'd still have a big problem, and that is, well, how the heck am I gonna convince a hiring manager or a recruiter to actually be interested in me? Because I have no prior experience, right? It seems every job description these days, even if it's junior or entry level, is asking for one year, three years, five years of experience and I have a big fat zero years of experience. So that is a problem. So there's this thing I created called the circle of doom. It basically says, "I can't land a data job because I don't have data experience because I can't land a data job." And it's this never-ending cycle. You never break out of it because you can't get a job because no one will give you experience, but you can't get experience 'cause no one will give you a job. And it's so frustrating, and it sucks. It honestly sucks. So comment if you've ever felt the circle of doom, because I definitely have. So how would I get out of it? How would I get out of the circle of doom? Well, I give myself my own experience, and there's three different ways that you can do that. Number one, projects. Number two, internships. And number three, experience shifting. I think projects is the best one to start with. Projects are the best way to showcase the skills that you've learned. If you're unfamiliar what the term project is, it's literally tangible evidence for hiring managers to know that you can actually do the things that your resume says that you can do. You can think of it almost as, like, practice work. You take a dataset, you analyze it as if you were a data analyst, and then you publish your analysis and results somewhere for them to see. And doing projects is actually a really great way to learn. Like, personally, that's the best way to learn for me because I'm very hands-on, I like realistic. And it's awesome because you're learning while you're building the proof to showcase to the hiring managers and the decision-makers, so it's a win-win. You could also do a project as an internship, and an internship is just a very applicable real-world project for an actual organization, but you're probably not getting paid for it. And they're a bit harder to find, which is why I let actually all of my boot camp students do an internship for me and my company, but you could probably find one in your neighborhood or maybe your cousin or something like that runs a business. Internships are really great And lastly, you can do experience shifting, which is where you take your prior experience and you reframe it as data experience. Basically, the goal is to change your prior work roles and titles and the bullet points on your resume and shift them into a more data-y context. So for example, if I was a customer service associate in the past, I could shift that title to something like customer service analyst on my resume. And if I had a bullet point that said I answered customer complaint calls, I could shift it into something like log customer feedback in Excel. Now, I would never want to lie, but most experiences can be shifted at least slightly to favor yourself to increase the odds in your favor, especially because there are so many roles in the data world that aren't data analyst titles that are really just data analysts disguised as something else. For instance, marketing analyst, business analyst, financial analyst, operations analyst, healthcare analyst. These are all just data analyst roles in different industries and with different domains. So often your prior experience is actually partially data experience. It just needs to be shifted on paper and phrased in the right way. And of course, this takes some nuance and practice to get the wording right, but when you use it, it is a very powerful technique if you can figure out how to do it correctly. So I'd personally learn Excel, Tableau, and SQL by doing projects with real-world projects and data sets. I'd do an internship with my boot camp or a local organization, and I'd shift my previous chemical lab experience to be more data-y. And that would help me learn and also give me some more proof to show hiring managers that I can actually become a data analyst. And all of a sudden, I have data experience on my resume and on my LinkedIn now, which is incredible. So moving on to step three, which is building a home base. See, if I made these projects, that still really wouldn't be enough because no one would know about them. I need to publish them. I need to give them a public home, and that's actually what we call a portfolio. You can do multiple projects and host them all on one portfolio. And there's lots of places to build your portfolio, to host your portfolio. You probably hear people talk a lot about GitHub as a portfolio option, and a little bit of a hot take here, but for data analysts, I kind of hate GitHub. The short explanation is that GitHub is not designed to be an analytics portfolio place. It's a code-sharing repository meant for engineers. And so it's really hard to get the hang of when you're fresh and you're not technical. And actually, for people who are viewing it, it's really hard to navigate, like, the pages and the... Anyways, it's just really not a user-friendly platform. So other good options are actually GitHub's portfolio product called GitHub Pages, I like GitHub Pages, or any website builder like Square, Wix, Carrd, those are all great as well. You could even use your own LinkedIn profile with their built-in project section. Or better yet, you can use their articles plus a featured section and host it on LinkedIn. I actually have my own platform that you can use. It's called My Datafolio that we built specifically for data analysts because I was literally teaching people how to build portfolios for the last five years, and there's pros and cons to every different platform, so we built our own to make it the absolutely best platform to build your data portfolio. So personally, I choose my Datafolio, and I build my portfolio there. Now, once I have this portfolio live, I need to make sure that I have it linked on my resume and on my LinkedIn so that people can actually see it. I might even create, like, some sort of a project section on my resume that has some of my projects with links on there. The goal is to try to get people to that portfolio so they can actually see my work. And speaking of resume and LinkedIn, that's actually step four. Step four is to hack my resume and hack my LinkedIn, and I would literally think of it as hacking. I'm hacking my resume and my LinkedIn to be the most ATS-optimized I possibly could. And I'll explain what that means here in just a second. But I would actually probably make this step two instead of step four. I feel like so many people, so many aspiring analysts wait to update their resume and their LinkedIn until they feel ready, until they've learned everything that they should learn. And newsflash, guys, you will never feel ready. You will never learn everything Remember the rules I told you at the beginning to tattoo on your head? Like, you don't have to know every data tool, and the few data tools you do know, you don't have to master. Like, that is coming into practice here. You will never feel ready, and that's okay. I still don't feel ready. I've been in this field for ten-plus years. It's important to update your resume and your LinkedIn early because these, along with your portfolio, are really the only things being used to judge you as a data analyst, to judge you as a job candidate. They're basically your professional business cards to the world, and if these aren't optimized, you will struggle to land an interview, even if you're a great candidate. And on the other hand, if you're an average candidate, but you have an amazingly optimized LinkedIn and resume, you're gonna land more interviews, that's gonna lead to more offers, and it's gonna lead to a higher salary. So how do you optimize your LinkedIn and your resume? Well, personally, what I would do is by making some of the changes to trick this applicant tracking system, the ATS, into thinking I'm a better fit for the role than maybe I am. And the ATS, if you're unfamiliar, these are the computer systems that read your resume when you submit it a job application, and honestly, most of them are pretty dang dumb most of the time. They're really just looking for the words on the job description and the words on your resume or your LinkedIn, and they're like, "How many words are the same? How good of a match is this person based off of the word match?" And it's not quite that simple. It's a little bit more complicated than that, but it's a good way to think about it. So if I wanted to apply to a data analyst job that requires Excel, Tableau, and SQL, and I don't have the term data analyst on my resume and I don't have the words Excel, the words, uh, Tableau, or the word SQL on my resume, I literally have no shot to get past the applicant tracking system. On the other hand, the more times I have those key terms on my resume or my LinkedIn, the better chance I have of passing it. And this is a technique called keyword stuffing, and it is possible to go overboard here, but I rarely see it from people. The majority of everyone listening, the majority of aspiring data analysts, they under-stuff key phrases like the title and the skills that they need to know for the job description. There's just not enough of it on their resume. So personally, I will keyword stuff the heck out of my LinkedIn and my resume, and I find doing some of that experience shifting that we talked about earlier makes this process a little bit easier and more natural. I'll also be sure to turn on a feature on LinkedIn called Open to Work, and this allows recruiters to know that I am interested in being considered for open data analyst jobs. Ninety-eight percent of recruiters are using LinkedIn, and so having this turned on is a real benefit to you. And once you have a portfolio and an optimized resume and optimized profile, you turn this setting on, and you will be surprised, you guys, to see how many recruiters are asking you if you're open to this job. Would you apply for this job? Would you ever consider this? And that's the power position we wanna be in. But of course, we're not just gonna wait there and wait for these magical recruiters to send us messages. We're gonna move on to step five, and step five is to take interview actions. And what I mean by that is take actions that lead to interviews, and that's a weird phrase. I totally get it. But what I mean by that is to mostly apply to jobs. And this probably won't be a shock to anyone, but Internalize it here. If you don't apply to jobs, you will not get interviews. I'll say that again. If you don't apply to jobs, you will not get interviews. If you don't get interviews, you will not land a job. And I see so many aspiring data analysts get stuck here. They're so analysis paralysis in their own head, imposter syndrome, that, you know, they've already learned a lot. They maybe even built some projects, but they can never get the confidence in themselves to feel ready to apply to jobs. And I-- look, I get it. Applying to jobs is really scary. It's frustrating. It sucks. It's annoying. But you know what else is sucky and frustrating and annoying? Is staying stuck in your current situation. So personally, I will be applying to jobs even if I do not feel ready, even if it's scary. And of course, I'll be using LinkedIn Jobs to apply for jobs, but I'll also be trying other job boards like Indeed and FindaDataJob.com. But applying for jobs, it's not the only action that leads to interviews, actually. And it might not even be the best action to take. There's another action I could take that's probably equally scary, frustrating, and annoying, but it will get twenty times the results. So what's the action, you might be wondering? It's networking And networking basically means talking to humans, meeting new people, talking to old connections. And I won't sugarcoat this, it will be annoying. But I will be networking my tail off. Networking has become even more important this year with so many rises of ghost job listings and so many job scams and all these AI automated applications. Networking's never been more important. And there's lots of different ways that you can network: attending events, posting on social media, sending messages, calling people, et cetera. I try a lot of them, but I know the best networking comes from people who already know, like and trust me. And those people, well, they're right here. They're in my phone contacts. I would literally go through every contact on my phone and send them a catch-up message. And it might be awkward, but I would try to make it not awkward. I'd say something like this. I'd be like, "Hey, how's life these days? Is work going well?" And eventually I would say, "Hey, I'm trying to become a data analyst. Do you know any?" And that's enough to get the ball rolling, like it's enough to actually get a conversation going that's not totally weird where you're like, "Hey, will you give me a job referral, please?" And I call this the phone book method, and it's just one way that I have tried to make networking easier. And I personally also post on LinkedIn a lot with my updates about my journey, what I'm learning, what's new in my portfolio, what is my latest project. I also probably comment on other big data people's profiles as well, try to get my profile some love and some attention. And I also would send hiring managers and recruiters cold messages pitching myself for the jobs that they're actually hiring for right now. And I'd find all the contact information in those jobs on premiumdatajobs.com, which actually lists data jobs that have a recruiter or hiring manager attached to them with their LinkedIn. And once again, even though this is step five, I try to be doing this throughout the entire journey 'cause this is the end part of the SPN method and it's the least utilized. People ignore it the most, but it yields the greatest dividends. So those are the five steps that I would take if I had to start from scratch and land my first data job in twenty twenty-seven. And remember, I'm a lazy person. I'm trying to do this the easiest and the fastest way possible. This whole process is called the SPN method. It states that you need to learn the skills, but not every skill, and only the right ones, and not all the way. Build projects that can showcase those skills and put them on a portfolio. That's the P part. And then you need to be networking, and this includes your resume, your LinkedIn, networking for you. And you need all three parts, the S, the P, and the N. And if you're missing just one, you can really struggle to make progress. You really need all three, the synergies to actually make true progress. And you can totally do the SPN method, follow the SPN method on your own, but it's more fun to do it in community, and it's a lot easier to do with a mentor showing you everything step by step how to do the details of each part. And that way you know you're on the right path. You don't have to second-guess it. And that's actually why I created the Data Analytics Accelerator Program, which is basically a 10-week boot camp to help you land your first data job, and we would literally go over all these steps together in detail, and I'd tell you exactly what to do for each one, so that way you're doing the right things to actually land your first data job. If you'd like to learn more about the SPN method and the Accelerator Program, you can head to datacareerjumpstart.com/daa or we will have a link in the description of this episode. I hope you guys found this really helpful. I'm praying that all of you guys land a job in 2027. Let me know if you'd like me to explain anything more in detail.