150: 9 Huge LIES About Becoming a Data Analyst Nobody Talks About

Here are the nine biggest lies about landing a data job that are being told this year. Lie number one, you need a computer science or a math degree. There's lots of people and organizations that will tell you that in order to land a data job, you need to have studied computer science, math, or economics in college, but that's not the case. Take me for example. I studied chemical engineering and became a data analyst and then became a data scientist. But even then, chemical engineering is pretty technical. There's a lot of people who have less technical degrees than chemical engineering who have landed into the data world. For example, I've interviewed a lot of them on this channel. We had Alex Sanchez who was a high school math teacher and he pivoted into data. We had Aaron Sheena who was a music therapist who landed a financial data analyst job at Humana. We had Rachel Finch who studied biology and now has a business intelligence job. And then there was Trevor Maxwell who doesn't even have a college degree and ended up landing a technical data analyst job. You don't need a computer science degree and you don't need a math degree. Whatever degree you have now is probably good enough. And if you don't have any college degree, you can probably do it as well. It's just a little bit more of an uphill battle. I have a whole YouTube playlist where I talk to people who land jobs without college degrees. I'll have that in the show notes down below. Truth be told, you don't need a computer science degree and you don't need a math degree to break into data analytics. Lie number two that they tell you is that you have to be good at math and statistics. And honestly, you don't really have to be good at either. Now I am going to caveat here and say if you want to be like a deep research data scientist. You probably want to be a little bit good at math, but for the rest of you guys who just want like a normal data analyst job, you honestly don't have to be that good at math. Like honestly, most of my students, when they actually land a data job, the math that they're really doing is mostly aggregations. That's like some average max min. This stuff isn't complicated. You honestly probably learned most of it in high school. You may have forgotten now, but honestly, it's kind of like riding a bike. Once you review it, you'll be able to catch up very quickly. Now, I can already hear all of you people commenting and being like, well, isn't statistics important? There's statistics in data analytics. And sure, there's definitely some statistics in data analytics, but I think most people overblow the amount of statistics you have to know. In fact, a lot of programs like data analytics master's degrees will say that you're supposed to know calculus and linear algebra in order to even like, Start the program, and that's just a flat out lie, like the amount of calculus and linear algebra that I use as a data analyst is very minimal. Can those concepts potentially help you? Sure, but it's not worth the amount of time that it takes to actually learn all that stuff. It's not worth it. Like you're not really going to benefit the return on investment, the ROI. Is not very high. Of course, there's things like AB testing, hypothesis testing and regression that are going to be useful for a lot of data analysts. But honestly, that stuff's not super hard to learn. And the majority of the time, like you're not doing the math, the computer's doing the math. So as long as you know what a hypothesis. test is and how to set it up and how to interpret the results, you're good. And honestly, I think you can learn that in one to two weeks. Lie number three is that you have to know everything about data analytics in order to land a data jump. That you have to know Python, you have to know Excel, you have to know SQL, you have to know Tableau, you have to know Looker, you have to know Power BI, you have to know SAS, you have to know R, you have to know Java. So on and so forth, and it's just not true. Honestly, you don't have to even know that much to be a data analyst, and maybe just one of those skills is enough. For example, I interviewed Matt Bratton on my podcast a while ago, and he is like in the C suite of the data world, and he basically only uses Excel. I've interviewed different people on my podcast. And sometimes they only use Tableau or they only use SQL. It really just depends. So sometimes you only have to know one data skill throughout your whole career. Now saying your whole data career, that's a little bit dramatic. Like you will probably use multiple skills throughout your career. But when you land that first job, like really a lot of the time, you're using one to two data tools, max. That being said, it's like, well, how do I know which one to two that those are? And you really don't. And it's going to change from job to job. But here's what I will tell you that Python is only required 30 percent of the time for all data analyst jobs from junior to senior. So personally, I don't really think it's worth learning to be able to apply to those extra 30 percent of the jobs when you're just getting started. I did an episode about this previously. You can see it right here and I'll have a link to it in the show notes where I really don't think you should start with Python or R to be honest. The lie is that you have to know everything. And the truth is you don't, you can get started today. And honestly, you can probably land a job pretty soon with The skills you have already line number three is that certifications matter. I don't care if it's the IBM certificate, the power BI certificates, the Google data analytics certificate. The truth is for the majority of data jobs, your cert does not matter. I know that might hurt to hear, and you might not want to believe me, but I actually run my own job board, find a job. com. And I analyze the 2000 plus jobs that I've posted on there the last four months. And not once did any of the jobs posted on there. Ask for any sort of certificate. I know like the badges look cool and like the certificate looks cool. The truth is no one really cares. At least employers don't really care. I have a lot of people who message me and they'll say, Hey, Avery, I don't need your bootcamp. I'm already data analyst certified. And that is like the biggest lie that you could ever say. And I understand that someone did. Certify you as a data analyst, but there's nothing in the industry that's standardized that makes you data analyst certified It's not like a nurse or a teacher where like you have a license. That's the wild west out here in the data world We don't care about that stuff. So having a certificate. It's not a bad thing necessarily But it's not like all that you might think it is. It's not your golden ticket into the data world It takes a lot more than that and that leads me to my next lie lie Number four is that skills are enough now you think that like If you want to be data analyst, you have to learn these X amount of things, and then you can become a data analyst, right? Wrong. Skills aren't enough when you're trying to land a data analyst position for multiple reasons. One, as data analyst, like you're actually not just spending your whole time using those technical skills. Like you're not just in Excel all day. One of the most important things you'll be doing as a data analyst is communicating, is working with stakeholders. Is talking to teams and leaders and understanding, you know, what the data is, where the data is at, what, how you should analyze it, what's important for them to know, so on and so forth. But two, anytime you're trying to land a data job, it's not the most skilled person who lands the job. Like think about it. I'm down here in my office. If I spent the next 240 years of my life just studying data analytics, but I didn't have a resume, would I land many data analytics jobs? Probably not because it takes more than just your skills. There's a whole variety of things that will actually help you get hired. I create a little mnemonic for you to remember. It's called the SPN method, and it's the easiest and fastest way to become a data analyst. S stands for skills, and that's one third of the equation. But it's only one third of the equation. You need the P and the N. The P stands for projects or portfolios. And these are basically opportunities for you to showcase your skills because anyone can say that they know SQL, but you want to back that up with tangible evidence to a recruiter or hiring manager via project on your portfolio. The N stands for networking and really like 70 percent of jobs are done through networking. You're really getting recruited or referred. And so there's a lot of different ways you can network and a lot of different things that you can do to increase your chance of getting hired. That is totally irrelevant and not even related to your skills. There is no correlation to how skilled you are, how quickly you land a data job, and how much you get paid as a data analyst. If you want to learn more about the SVN method, I'll have a link in the show notes down below. Lie number five is that AI is going to take your job. It's really interesting because a lot of people are nervous about becoming a data analyst because they don't feel like it's very AI proof. And one thing I've been thinking to myself is Okay. Well, what careers are AI proof? In fact, I had one perspective student. He was messaging me and saying that his friend was kind of making fun of him because like data analysts are going to be replaced by AI and he had like a blue collar, more like mechanical job. And that was never going to be replaced by AI. I think that's interesting because like throughout history, haven't we seen like more of the mechanical jobs being replaced by AI? previously. So like, I think those jobs aren't safe. And then I thought, oh, maybe like a doctor that I was like, well, aren't like a bunch of like robots doing surgeries nowadays. And like, can't you just kind of like use web MD or whatever chat, GBT to like ask what's wrong and get a diagnosis. Obviously there's going to be some jobs like nurses, for example, where I think that is basically impossible to have a robot or AI do. But honestly, I've used AI to try to analyze data and it's definitely not great. Another thing you should realize is the difference between augmentation and automation using AI. Augmentation is almost like you can think of it like putting on like the like glove in Iron Man or something? I don't know. I'm not good at Marvel, you guys. Uh, like, like the Infinity Stones in that one movie, right? Like, that changes who you can be and the powers that you have, but you're still yourself. And then the other one would be like, no, I create a robot that's super powerful and it replaces me completely. And honestly, AI is going to augment you. That's for sure. It's going to change how work is done. But it's still you doing the work a lot of the time. I've seen a lot of these companies try to come out with like the auto analyzing data and it's not great so far. Is it going to get better in the future? Yes, definitely. But I definitely don't see the human element getting taken out of it anytime soon. The ability to reason to actually find like what's relevant to the business and then explain all that back to someone I think is something that's very valuable. I'm a data analyst, right? I teach people how to become data analysts. So my future is very heavily tied in this and I honestly am not that worried about it. I think that, AI is going to help us be better data analysts, and that's about the gist of it. So lie number five is that AI is going to take your job. Lie number six is that you're going to spend 80 percent of your time cleaning your data. I don't know where this came from, and I don't know who made it, and I don't really know who propagates it further. Personally, in the roles that I've been in, sure, data cleaning is important, and it does take a significant amount of time, but it's nowhere close to 80%. Honestly, if you're spending 80 percent of your time cleaning data, You're probably spending your time on the wrong things. I honestly think that like 80 percent of your time should be spent talking to people as a data analyst before you start a project, when you're in the project and after the project, I think communication is actually way underplayed in the data world. But I don't know who's saying that 80 percent of your time is cleaning data because that's. A huge exaggeration. Data lie number seven is all data titles, uh, and I'm just so sorry for all you job seekers out there. This is the most frustrating thing on planet earth, but once again, the data world is the wild wild west and basically job titles are all kind of made up in the data world. There's kind of like the big three. There's the data engineer, the data analyst, and the data scientist. But there's so many more positions in between that overlap and that are the same and that are misclassified and companies will call something, you know, a data analyst one place, but that's really a data scientist other places. And it's really confusing. So all the data titles you're reading on the job board are probably lies. And you should try to base it off of what's like in the requirements section of the job description to actually know what the job is going to entail and what the actual title kind of is. For instance, there's something called a data science analyst. I don't know what the heck that is. I've even seen data analytics scientist. Technically, my role at Exxon for a long time was optimization engineer, but I was really doing the work of a data scientist. And even at my first job, I was technically a data analyst, but you could have also called me a chemometrician. There's so many different titles. They're so confusing. Honestly, I've CEO reach out to me one time and ask me to look over. Their job description for hiring their first data analyst. I looked it over and I was like, this is a data scientist job, not a data analyst job. And he replied, well, what's the difference. And this is like, not a super small company. Like this is definitely a company you've heard of before. I guess it was technically like a general manager, not the CEO. It was like the president of a local area anyways, but still like that is pretty crazy. Right. The people who are writing these job descriptions maybe don't necessarily know. a hundred percent what they're talking about. Lie number eight is that there is lots of remote jobs. And now this one's super interesting because anecdotally, it does feel like there is a lot of remote jobs. Most of my friends who work in data have pretty flexible schedules and lives for the most part. And most of my students in my program get pretty flexible jobs. But when I went and actually did the research myself and I started web scraping job listings. I found that remote jobs only make about 16 percent of all the jobs on the market, meaning the other jobs, the remaining 85 percent ish are not remote. And obviously most of you guys watching probably are interested in a remote job. So let's say that 95 percent of people are interested in a remote job. That means there's a demand 95 percent for a low supply of 15 percent of jobs that are actually remote. And this is one of the reasons why the job market is so crazy right now and really frustrating and it feels like it's impossible to land a day job. The truth is there's just not as many remote jobs as you may think there is, but there's actually equally the same amount of hybrid jobs. So there's about 15 to 16 percent of jobs in the market that are hybrid. And the cool thing about hybrid jobs is it's on a spectrum of being in the office and working from home, and every hybrid job is somewhere on that spectrum, but in different places. Some of my students work from the office four times a week and then work remotely one day of the week. Sometimes it's reversed. Like for instance, some of my students who work at Humana, they work from home four days a week and they work in the office one day a week. I even have one student who is hybrid, but she's only required to go in the office once a quarter. Now, to me that's more remote than it is hybrid, but it was still labeled as hybrid. So I think the biggest play and what you guys should be focusing on right now is hybrid jobs. Lie number nine is the self taught data analyst or the self taught data scientist. So many people will say I'm self taught. And first off, what the heck does that even mean? Like you're learning from somewhere. It's not like you just like went out into your yard and like really thought hard and you're like, Oh, yes. What if I like Excel and Vlookups would make a lot of sense in a pivot table? Yes. Oh, and joins and SQL. That makes a lot like you're not just like divinely absorbing this knowledge. You're learning from somewhere, whether it's a book, whether it's online, so on and so forth. I think most people say self taught because they maybe don't have a formal degree or something like that. I would consider myself. Self taught, but I eventually got a master's degree in data analytics in college. I took statistics classes that got me really interested in data. I had a really good mentor at my first job. He taught me a lot. So I think the concept of, I want to be a self taught data analyst is kind of silly. It's also like, you don't get sent a trophy for being a self taught data analyst, like who cares if you're self taught or not? Like you don't get to wear like a badge. It's like, Oh wow. Like she's self taught. He's self taught like now, like it's okay to be. You know, not self taught like that's totally acceptable. And honestly, maybe you should wear that as a badge of honor. It's like, no, I didn't do this on my own because I knew I needed help or I wanted to do this faster. So I sought help like there's nothing wrong with that. That's plenty cool as doing it yourself. So there you have it. The nine biggest lies of becoming a data analyst and landing a data job. Are there any myths that I missed? Put them in the comments down below and I'll try to respond to every comment.