136: How I Would Become a Data Analyst In 2025 (if I had to start over again)

Here's how I would become a data analyst if I had to start all over again in 2025. Now, I'm lazy and I'm impatient, so this method that I'm going to be choosing, the SPN method, is the fastest and it's the lowest amount of work to actually land a data job. But it still is a lot of work. Step one is I'd understand the different data roles available in the data world. There are so many different data roles, and it's not just data analysts. There are so many other roles, That are just like data analysts, but have slightly different names and slightly different responsibilities. For example, business intelligence analyst, business intelligence engineer, technical data analyst, business analyst, healthcare analyst, risk analyst, price analyst. There are so many, literally so many different options that you could possibly choose from. And they're all pretty similar for the most part, but some things are going to be slightly different. So for example, a healthcare analyst, you're going to be a data analyst. But specializing and looking at healthcare data. Financial analysts, same thing. You'd be looking at financial data. A BI analyst, like a business intelligence analyst, and a data analyst, really a lot of the time are going to be doing the exact same thing. So it's important to be looking for all these roles, understand what these roles do and what their slight nuances are, because there's a chance that your previous experience is actually valuable and would help you get a leg up in applying for these different jobs. So for example, If you have a business degree and you're trying to transfer into business analytics, becoming a business analyst makes a lot of sense or a financial analyst makes a lot of sense. If you've worked previously as a nurse or like a CNA, maybe you become a healthcare analyst. Whatever you've done previously, there's probably a good chance that that experience is valuable in the data world to a specific role. So even like I have a lot of truck drivers in my business. Bootcamp. Those truck drivers can be logistics analysts, they can be operations analysts, they can be supply chain analysts, because their previous experience is actually valuable. The second thing that I would do is figure out what is actually required, because here's the truth. There is actually thousands of data skills and tools. and programming languages out there, but if you try to master all of them, you're going to be like 150 before you feel prepared to start applying to jobs. You're going to be dead. It is impossible to learn. It's impossible to master all the different data tools and skills and languages. So by default, have to choose a few. Now you have a decision to make is which ones do you choose? And I, like I said, I am lazy and I want to do the least amount of work possible. So I believe in the low hanging best. Tasting fruit analogy. If you can imagine that there's a tree that has some sort of like a peach or an apple on it, right? The easiest fruit to grab is always going to be the closest, so it's the lowest hanging fruit. But not only do you want the lowest hanging fruit, you want the tastiest fruit, right? So this is stuff that is not only easy to learn, but is extremely useful. Those are the things you want to focus on. Out of the thousands of data skills, those are the ones you'll want to focus on. You can do the research on your own, if you'd like, by looking at job descriptions and writing down what is actually required, but that's a lot of work and you can take it from someone like me, who's been in this space for about a decade now, looked at literally thousands of job descriptions. I even have my own data job board. Findadatajob. com. And I look at it all the time to see what is being required. So I've done this research for you already, and I will have a link to my conclusions in the show notes down below, but basically what you need to know in terms of low hanging fruit, it's Excel, Tableau, and SQL. That is it. Those are the top three skills that you should be learning as a data analyst when you're just trying to get started. And if that is too hard to remember, you can remember every turtle swims, right? That's easy. Excel. Tableau and SQL. That is where I'd start and I wouldn't really veer off of that until I've landed my first data job. Now you might have noticed that I didn't say Python and that might come as a surprise to many of you because you hear so much about Python and how cool it is and how popular it is and it is really cool. It can do so many different things. It's so powerful and it's actually my favorite data tool but it's actually only required on 30 percent of data analyst roles and it's really hard to learn. It takes a long time to learn Python because Python is hard, but also all programming is hard. And if you don't have a programming background, it's going to take a long time to just kind of even get your foot in the door in the Python world and understand what's going on. What's a variable? What's a loop? What's a function? Those types of things just, they take time and so if you only need it for 30 percent of the jobs, that means 70 percent of the jobs don't require it. And once again, I am all about doing the least amount of work possible and doing it as quickly as possible. So I say save Python for after your first day at a job because it's really just not needed to land that first one. Once again, I have a free video that kind of explains what skills you should learn and in what order and why. I'll have that in the show notes down below. The third thing that I would do if I was trying to become a data analyst is try to figure out how I'm going to convince a hiring manager or recruiter to hire me, even though I have no prior experience. There's this thing called the cycle of doom, which basically says I can't land a data job because I don't have experience because I can't land a data job. And it's this never ending cycle of, well, you're never going to get a job unless you have experience. You can never get experience unless you get a job. It's kind of like the chicken or the egg, you know? So you have to figure out, how am I going to beat the cycle of doom? And how am I going to convince someone that, yeah, I am a data analyst and you should hire me. How would I do it, personally? I'd build projects. Projects are a great way that you can demonstrate your skills. It's basically the tangible evidence for people to know that you can do what your resume says you can do. If you're unfamiliar with projects, It's like almost doing pretend work where you're pretending that you're working for a certain company. You take a data set and you analyze it and publish your results. We'll talk about where to publish them here in a second, but basically it's allowing you to learn with realistic data with realistic problems, but also you're creating some sort of evidence, like literally physical evidence that you can show to hiring managers, recruiters, and be like, Hey, look, I can do these things. I can be a data analyst. I can use Excel. I can use SQL. I can create a data visualization in Tableau. Once I understand those three things, the fourth thing that I would personally do is start learning. And I want to emphasize this is not the first thing. This is not the second thing. This is not the third thing. It's the fourth thing that I would do is start learning. And I would start learning Excel, Tableau, SQL, every turtle swims, right? And I would do that by building projects, because I think building projects is the most realistic way to learn. I'll think it's It's the funnest way to learn because just doing like pointless exercises on like these like interactive online learning things, this is not realistic. Like in real life, you're going to be having real data sets. You're not going to be in some like controlled environment. You're actually going to have to be analyzing real data that's messy, that has issues that has flaws and you have to figure it out. And so building projects is the best way to learn because you're also creating this tangible evidence that you're going to be able to show to hiring managers and recruiters. You might be thinking, well, where do I get started? Well, you need to figure out where you can find datasets. You have to have a good dataset. I just did an episode on this recently, and I'll have the link to the show notes down below. But the simple answer, the one word answer is Kaggle. Kaggle is the best place to find a dataset. It's not the only place, and there's other great resources, but if you're only looking for one, Kaggle is usually the place I would go. And I'd personally build projects based off of what you want to do ultimately. So go back to step one and think about it. Like if you have a business degree, let's say you want to become a business analyst, I would try to build projects that are relevant to, to business analytics. Maybe data on sales or marketing or operations, anything that's business related. Those are the projects I would try to seek out. Or if you're not sure, like if you want to be a business analyst or a healthcare analyst, or maybe you don't even care. You'll just take whatever you've got. I would suggest doing projects on lots of different industries. Maybe dip into healthcare analytics. Maybe do some people and HR analytics. Maybe do a project on manufacturing and engineering data. That way you're getting exposed to multiple different industries, so you can kind of figure out maybe what you're interested in. You're creating a robust portfolio that will be attractive to every industry and multiple companies, right? Because if you just focus on creating, you know, business projects, but let's say you want to become a healthcare analyst, it's like, oh, those projects don't really match up. So. That way you have a project for whatever role you might be interested in. So that's particularly what I suggest doing. And it's what we do inside of my bootcamp, the Data Analytics Accelerator is we learn Excel, SQL, and Tableau by building projects. And we built multiple projects in different industries. So that way we're very robust as can. The fifth thing I would do if I was trying to become a data analyst. is create a home for my projects. And this is actually what's called a portfolio. You know, projects are something that we do but if you just do them and you don't publish them and you don't share them, they don't actually do much good. You need to create a portfolio to home these projects. And the portfolio platform you'll hear the most about is GitHub. And I have a controversial take that I'm not a fan of it. I don't think GitHub is meant to be a portfolio. Now that's me being a little bit picky, but I just don't think it's the best option if you're choosing from scratch. What you need to do is make sure that your readmes are really good, because if you have a good readme on your GitHub, then it can work. But if you're starting from scratch, I recommend doing something like LinkedIn, using the featured section. Or choose GitHub Pages, which is from GitHub, but kind of a separate product, and it's their portfolio solution. It's actually what GitHub recommends as a portfolio. Or I really like Card, C A R R D. It's just a simple website builder, be really great options inside the accelerator, my bootcamp, so any of those three would work just fine. The sixth thing I would do is make sure that my LinkedIn and resume are up to date and optimized. And I would do this early, even before I've actually mastered Excel or I've, you know, tackled Tableau. The earlier you do this, the better, because. Your LinkedIn is your professional business card to the world. One of the really cool things is LinkedIn has a feature called Open to Work. There's two different settings on it. We can talk about it later, but basically you can have Open to Work for the entire world or you can just have Open to Work for recruiters. And either way, if you set up your LinkedIn correctly, your LinkedIn can start to work for you. And instead of you going out and applying for jobs, recruiters and hiring managers are actually applying to you for specific jobs. They'll reach out to you and be like, Hey, I think you're a good fit for this job. So having an optimized LinkedIn is, is really key. And then of course, having an optimized resume is a must because once you start applying for jobs. If your resume isn't optimized, you're probably not going to get many interviews. And the reason is there's so many candidates trying to get into data analytics roles, especially the entry level ones, that recruiters and hiring managers have to use what's called the ATS, which is the Applicant Tracking System. And basically it's, it's computer, it's AI, it's It's actually not even really that complicated, but there are certain things you need to do on your resume to have it be optimized and ATS friendly, so you can get past the computer screening and actually have a human being look at your resume, because it's so frustrating when you get rejection after rejection after rejection that you don't even know if a human's looking at your resume. A lot of the times you're just getting rejected by the ATS, and so you need to make sure you have an optimized resume. So, in terms of having an optimized resume, it would basically look like not having any columns on your resume, or any tables on your resume, and then using really key words that match the job descriptions, so that way you appear as a good applicant to the ATS. The seventh step that I would take is to start applying, and I think this is obvious, but a lot of people don't ever start applying for jobs. And I get it, because it's scary. How do you know if you're ready to land a data job? It's hard to know, and you probably will never feel ready, so I suggest just start applying anyways. And when you start applying, don't only apply on LinkedIn jobs. LinkedIn jobs is where everyone applies, and there's going to be hundreds of candidates in a matter of a few days on those platforms, the majority of the time, because everyone's doing that. So you might want to try something new, like going to company websites or checking out my job board, findadatajob. com or some other combination of other job websites. The point here is you need to be looking at multiple places and actually start applying. I know it's scary, but just do it scared. The next step I would do in this process is I would really try to be networking. And I, I would try to be networking the entire time, like even in step one. But this is where I fit on today's roadmap is step eight. So it's way easier to get hired when you know someone. In fact, my brother was just recently looking for a job and having a hard time and he ended up Getting an interview and landing that job because his wife's friend works there. And like, I can't tell you how often that actually happens. So networking doesn't have to be hard. You can do it on LinkedIn by posting and commenting on LinkedIn. I think that's really important to do, but I understand that's hard and a scary step. One thing that's really a lot easier is just to talk to your friends and family. Just say, Hey, I'm trying to become a data analyst. Do you know anyone who's a data analyst? Does your company hire data analysts and have a conversation? You're not even really asking them anything. You're just opening a conversation. I know this is hard and I know it's uncomfortable and I know it's not fun. Like it's much more fun to learn data skills than it is to network, but honestly, networking gets you the same, if not better results than upscaling and actually learning new data things. So you can't be ignoring this. Couldn't be ignoring this. I have to be networking, no matter how hard it is. Now, if all is going well, and I'm doing all the previous eight things that I've talked about, I think at this point I'd probably start to land interviews. There's two parts to an interview, the technical and the behavioral. The technical interview is when they're going to be asking you questions about data skills. It might be like, Excel questions or data visualization questions or oftentimes sequel questions and I'll ask you to write certain sequel queries This can be really scary and intimidating and honestly, they can be really hard The cool part is they don't always occur or or if they occur they occur very easily Sometimes they're very hard. Sometimes they're very easy. It really just depends and to prepare for the technical resources There's a lot of things that I could do. There's a lot of resources out there that would help me prepare. Um, there's something called Scrata Scratch that I'll have a link in the show notes down below that you guys can check. There's Data Lemur. There's a bunch of tools that will help you prepare for these technical interviews. Behavioral interview is going to be more like them trying to feel for who you are and what you've done previously and like how you would act as a human being, as an employee. And that is a little bit harder to prepare for because it's more of like, instead of answering technical questions, it's answering like personal questions. There's not a whole lot of resources out there. One of the things you would want to do is use the STAR method. You want to answer every question by saying, this is the situation I was in, this is the task I was given, this was the action I took, and this is the results that came from that action. And if you answer using that method, most of the time you'll be good. It can be scary, and there's not a whole lot of resources out there for this. So do you want to check out one that I made? It's called interview simulator. io, and it basically helps you practice these questions where I'll ask you the question via video and you will respond via video. And then we'll actually grade your answer and tell you what you did well and where you could improve. It's a pretty cool software. I'll link for that in the show notes down below as well. Wow, lots of links in the show notes, so be sure to check those out. So those are the nine steps that I would take if I had to start from scratch and land a day job in 2025. And remember, I'm lazy, I'm trying to do this the easiest way possible. This is This is what I call the SPN method. You need to learn the right skills, not all the skills, but the right skills. You need to build projects and put them on a portfolio. That's the P part. And then you need to be networking, updating your LinkedIn and updating your resume. That's the N part. And it's the easiest way to land a data job. Now you can do all this stuff that I told you on your own and you'd be 100 percent okay, but it's a lot more fun to do it in community and it's a lot easier to do with a coach. Once again, I'm all about doing it fast, And it's much easier to do that with a given curriculum where you don't have to be questioning. Am I doing this right? How do I actually do this? So on and so forth. And so that's why I created the data analytics accelerator program, which is basically a 10 week bootcamp to help you land your first day at a job. We'll go over all of these nine steps. Hand by hand, step by step together, and make sure you're ready to land a data job. If you want to check that out, you can go to datacareerjumpstart. com slash D A A D A A standing for Data Analytics Accelerator. And of course, I'll have a link to that in the show notes down below. Let me know what I missed and what questions you have. I'll try to respond to everyone in the comments down below if you're watching on YouTube or on Spotify. And I wish you the best of luck in 2025.