195: If I Wanted To Become a Data Analyst in 2026, This Is What I'd Do

If I wanted to become a data analyst in 2026, here's exactly what I would do. I'd break it down into two separate parts. Number one is the, what do I actually need to know to become a data analyst? The skills, and number two is how do I actually stand out first, what do you need to learn? Where should you learn it? The biggest thing when it comes to becoming a data analyst is actually knowing the tools to analyze the data. Not only knowing the tools, but also having the mindset of a data analyst. And in my opinion, those are actually both learned best through doing projects and real life examples. A project is basically like a use case or an actual like real world example of you analyzing data. I'm not a really big fan of like all of these silly online tutorials and all these silly like, oh yeah, like step-by-step. This is this little sandbox and this is how you do this and this is how you do that. I just don't feel like it's super realistic. I personally. Learn best by doing hands-on real practice. And so I think why not just start with that? I also wouldn't learn everything because there's so many different things you could be learning, and my old philosophy is we need to get your foot in the door as quickly as possible so that way you can start to get paid. To learn on the job. Like if you're going to try to learn everything before you feel ready to apply to data analyst jobs, you're gonna be like 89 years old before you start applying for jobs. And, uh, I don't know about you, but I don't wanna be working when I'm 89. So you first need to shrink the amount of things that you're going to learn. So in my opinion, the first thing that you should learn is excel, just 'cause it's really easy and it's really in demand. Second thing you should learn is Tableau or Power bi, because they are also very in demand and pretty easy to learn. And the third thing you should learn is sql, because once again, pretty easy to learn and pretty in demand. I think you should put R and Python on the shelf. And honestly, anything, any other tool really on the shelf right now, just start with those big three, uh, BI tool, Excel and sql. Ignore everything else because it's just gonna keep you in the study tutorial. Hell, for a long time. Not only would I be learning those things, I would just be trying to learn to think as a data analyst, so when I get a data set, you know, what does it actually mean to clean the data set? What things should I be looking for? Or in the data set that might make it dirty or hard to analyze. Then I, based on the data set I have, I would start to already think through what are the different things that I can do with this data set. So, for example, if you have a date column, you automatically know, hey, this is some sort of a time series data set. I can do some sort of a time series analysis, like create a line chart or do some sort of predictive modeling like arima to predict, you know, how this trend will continue down the road. You know, five Decembers from now. Like what is their numbers actually going to look like. If you have categories, you can say, okay, like. What was this quantitative variable, like some sort of a price or some sort of a number? How does that number change the different categories? So like if you have like a blue product and a red product, like did the blue product sell more or did the red product sell more? You know, what were the different margins on each of those? Like those are the different things that you can start to try to learn to think as an analyst. In my opinion, it's actually really hard to learn to think of an analyst. Learning the skills is not easy, but it's a lot easier than learning to think like an analyst. The only way I really try to teach other people to think as an analyst is one, give them realistic examples where they can actually go through the process step by step of like, oh, okay, I kind of get this. I kind of get this. And the other one is what I call project hacking, which is basically you see someone else's analysis and read through what they did, and you think, oh, okay. And you do that enough. Eventually you start to think like an analyst, like there's no magic bullet, there's no framework. I can give you right now that's gonna help you think like an analyst. It just comes with experience. And experience is just the amount of time that you, uh, have hands-on analyzing data or hands-on reading someone else analyzing data. I think those are like the only two ways that you can start to think like an analyst. Now, I'm not a big fan of certificates because I actually don't have any certificates. Like I don't have the Google Data Analytics certificate. I don't have any like Comier, TIA certificates, I can't even say the name, right. I don't have an IBM certificate. I don't have a meta certificate. I literally have zero certificates, landed all of my data jobs in my corporate. Career without a certificate. And I don't think certificates are really built to teach you the best because I don't feel like they're very hands-on project DI feel like they're kind of unrealistic, kind of handholding baby. Uh, and I just think people like are like, oh, like I need a certificate to be a data analyst. No, you don't. You literally don't, and if you think that having a certificate is gonna make a difference, I don't think it is because it's never made a difference in my career. I've talked to a lot of hiring managers and recruiters, they don't really care. There's no like standardized certificates to have in data analytics field. So personally, I think doing hands-on projects is infinitely better than doing these silly certificates. That actually leads me into the second half, which is okay, let's say you learn Excel, you learn sql, you learn Tableau. Great. But how do you actually stand out now? Because the data analyst job market is very competitive, especially kind of at that entry level junior data analyst role. Like how do you actually stand out? And I think there's a couple different keys here. I think one of them is to actually broaden your job search. Because literally you guys, everyone is looking to become a data analyst, but there's like literally 20 different titles that you could be searching for that aren't exactly data analysts, but basically they're a fancy title to say that you're a data analyst. I can't list them all right here, but business intelligence engineer, financial analyst, product analyst, pricing analyst, a lot of things that have the word analyst in it are going to be good for you. Um, because a lot of the times it's just like, hey. Data analyst role plus domain smash together, get a new title. And a lot of people aren't looking for those roles. So they're less competitive, but they're literally the jobs you're looking for. Now I have proof of this because I actually run a data job board. It's called Find a Data job.com. We post about 30 data jobs a day. Um, we try to include a lot of these like kind of alternative data analyst roles. And I actually have all the data, all the click data of you guys going to that website and what do you click on? And it's actually unproportional data analyst roles. Like if it literally says data analysts, those get like, I think two to three times more clicks than like a financial analyst or a business analyst or a marketing analyst or a pricing analyst or something like that. And literally, the job description could be the same. It's just everyone loves the title data analyst and that's what they're used to. And we just love what we're used to. So I would say. Try to apply less to like data analyst roles and more of these like niche terms that basically mean data analysts but aren't data analysts. The other thing you're gonna have to figure out is like how do you convince the hiring manager or recruiter that you can do what a job description says that, that you need to do because you don't have prior experience, right? So it's like the cycle of doom, right? I can't get a data job because I don't have data experience. Well, why can't you get data experience? It's like, oh, it's 'cause I don't have a data job. So you're in this never ending cycle and it's like you need someone to take a chance on you. But in order for someone to take a chance on you, you actually need trust. And trust is hard to get, especially when you don't know someone, right? We trust people that we know, and you're going to be applying to recruiters and hiring managers who don't even know you at all. They don't even know your face. They hardly know your name. They just know A PDF that you gave them with some stuff on it. So that's where like. Really doing a good job on your resume, really doing a good job on your LinkedIn, maybe trying to send cold messages and actually networking, right? Like if you can get a cold message to a recruit hiring manager that they actually like, that puts you so much far against all the other competitors. If you can have like a friend or a family member kind of refer you, then all of a sudden you're more trusted. 'cause they probably already trust that person. So like you need to come up with a game plan to actually get trusted by a recruiter or a hiring manager. And that is hard to do, like I said. Updating your resume, making it really good, making sure your LinkedIn is really good. And then the last thing I think is creating a portfolio. So remember we talked about those projects earlier, how it's the best way to learn, creating those projects and then putting 'em on a online portfolio where it's like, Hey, this is literally tangible evidence that I can do the what the job description's asking. Like the job description right here, like look at here is a project. That I did that basically nears what the job description is. And that way you're making yourself a little bit of less of a risk. It's like, Hey, look, hiring manager, you're worried about me. I've already done this before and here's the proof. So like, go ahead and take a look at the proof right here and uh, trust me I can do this. And creating portfolio is very good for those use cases. So I think creating portfolio and networking really come in clutch here. Unfortunately, I think most people when they try to become a data analyst, they do one of these certificates where they learn some skills in some sort of like lame tutorial, or they get like their certificate at the end you're like, yay, I'm all certified now. Or they do something like data camp or something like that, and I just think you're missing out on projects. I think you're missing out on portfolios. I think you're not spending enough time. Emphasizing your LinkedIn, your, uh, resume and your networking and your cold messaging skills. 'cause like those things are really important because everyone knows at this point, everyone knows how to analyze data in Excel. Everyone knows how to analyze data in Tableau, like what makes you different. And that's really what's gonna make you stand out is having a portfolio, a good LinkedIn and a good resume. So personally, that's kind of what I would focus on instead of what most people are focusing on right now in this 2026 era of where it's really competitive to actually land a data job. If this resonated with you and you're like, yes, I want to actually do this. I'm interested in creating a portfolio. I'm interested in only learning some of the skills and then learning on the job and getting paid to learn. I actually run a bootcamp where I teach people exactly how to do this from zero to landing their first shaded job. We'll create a bunch of projects together. We'll put 'em on a portfolio. We'll update your LinkedIn, we'll update your resume, give you templates for both of those, and help you network in cold message recruiters and hiring managers to actually land that first data job. If you're interested in learning with me, you can check it out at data crew drums sot.com/daa, or you can go to the show notes down below and click to learn more.