226: 10 Things I Wish I Knew When Starting as a Data Analyst - Audio

These are the 10 things I wish I knew when I was just getting into data analytics, having been a data analyst for 10 years now and helped thousands of people transition into a data analyst role. Number one is there is lots of data titles that aren't just data analyst. A lot of the times we're like, "Oh, I wanna become a data analyst," but we don't realize that financial analyst, business analyst, healthcare analyst, operations analyst, data visualization specialist, data visualization engineer, business intelligence engineer, business intelligence analyst, that all of these are really just the same job resp- Description and requirements and responsibilities with a different fancy title based off of what industry you're in and maybe what company you work for. A lot of these titles do the exact same thing with just a different industry or maybe with a different tool. And really, like, you would be... If you would like a data analyst job, you'd be stoked with any of these jobs as well. So don't just pigeonsh- hole yourself into only looking at data analyst jobs exclusively. There's so many other titles than just data analyst, and I did that at the beginning, and I really regretted that, and it's really just because no one ever told me that there was more roles than just data analyst. Number two: You don't need to learn every single data tool, and there's so many out there. There's, like, literally thousands that you could possibly learn, whether it's, you know, the ones you've heard of, Excel, SQL, Python, Power BI, Tableau, R, AWS, you know, and then there's SAS, and then there's JMP, and then there's Qlik, Qlik, and then there's Google Data Studio, and there's Looker, and there's so many different tools that you could be learning, guys. You know, as a beginner, you're, like, overwhelmed 'cause you're like, "I know nothing. I don't even know what half of those things are." In fact, those might just be Pokémon he just listed, not even real data analyst tools. Those are all real data analyst tools, just for the record. But my point here is there's so many different tools, and it's gonna take you so long to learn all of them that you're just gonna feel really discouraged if you try to learn them all. And so my advice is don't learn them all. Learn the lowest hanging fruits, the ones that are the easiest to learn, that are most in demand, and it ends up being Excel, SQL, and a BI tool like Tableau or Power BI. I have a whole chart that I've actually shared with my newsletter before about the most in-demand jobs and how easy they are to learn that I've sent out in my newsletter. So if you're not subscribed, make sure you're subscribed to the newsletter at datacareerjumpstart.com/newsletter. It's absolutely free. I send a new episode every single Wednesday. Okay. Number three: Your domain knowledge really matters, and whatever career you've had in the past or whatever you studied in college is probably useful in the data world. Like, you might be an education teacher and you're like, "Oh, like, all of this, you know, studying and all this previous work was an absolute waste." That's just not the case. Like, your domain knowledge is really useful and really powerful, and if you combine your domain plus data, you're gonna be a superhero. You're gonna be, like, deadly analyst. Like, you're gonna be able to analyze things that most data analysts wouldn't be able to do. It's just because you understand the domain and you understand the know- the business knowledge and the industry more than just, like, some random data analyst would, and that sets you apart, and it gives you a really big advantage. When I was a data scientist at ExxonMobil, I was not the best data scientist at the company at all. There was people with PhDs in computer science, PhDs in mathematics, and they could out-theory me. They could out-code me. They could out-data me in so many different ways. But with my chemical background, I was pretty good at analyzing chemistry data 'cause I, you know, had studied it for four years in college, and I knew it like the back of my hand. And so I knew things automatically. I could see things in the data that that would take them, you know- days, weeks, months to realize. So your domain is your superpower, it's not your weakness. Number four, data analytics is just not head down coding, head down technical analysis. It's actually very, uh, collaborative. There's actually very, like, you have to talk to people, you have to get business requirements, you have to think about what you're actually doing. It's not like you're just, you know, at your desk all day, "Boo, I'm analyzing data." It's, it's a lot more social than that actually. You need to talk to stakeholders on the front end and on the back end and in the middle to make sure that you're actually solving the question that they are trying to get answers to. Because we're not analyzing data for funsies out here, guys. It's not just like, "Oh yeah, let's make a chart," 'cause we wanna make a chart. Like, charts are cool, but none of us wanna be, like, making charts all day. We wanna make charts so that we can understand what's going on in our business, so we can understand the swarm and sea of numbers in a manageable human way, uh, via data visualization. And so you really need to be, you know, like this. And if you're listening to the audio version, I'm, like, doing something weird with my fingers. We're, like, really close to each other with your stakeholders so that you are actually answering business questions for them and helping the business move forward. Number five, it's just that SQL's really important, you guys. When I first got into data analytics, I thought Python was everything. Everyone's like, "Oh, Python, it's so cool. Python, it's like the new tool. Everyone's using Python." And Python's great, and I love Python. But just know that SQL is really important. If you've never heard of SQL before, it stands for structured query language. And basically It's the most used data tool on planet Earth. Now, if you read my newsletter, you know that I... 50% of data analyst jobs require Excel, and that's more than that require Sequel. So why am I saying it's more important? Well, it's because data scientists and data engineers use Sequel a whole heck of a lot more than they use Excel. So in the grand scheme of things, in the big data career world, looking at data analysts, data scientists, and data engineering, Sequel is the number one tool. In data analytics, it's just Excel, but then Sequel is number two. So I just wanna emphasize how important Sequel is. At the beginning of my career, I didn't really realize how important it is. Um, I kind of just ignored it. In fact, I went through my whole first data job without ever using Sequel, and that is, like, a little bit embarrassing to mention. But it's also important to realize that some jobs don't require Sequel. But I wish I would've used Sequel at that job because it would've just managed our data better, faster. It's just the best way to organize and query your data. All right, number six, and that is that your personal brand and networking really matter. When you're trying to land a job, either your first data job or your second data job or your next data job, like, having a personal brand and networking really matters because you're just gonna have every advantage. Especially now where the applicant tracking systems have so many different applicants, it's really hard to stand out. And so if you actually have a human-human interaction or if someone knows your name, if they know your face, you're so much more likely to get the things in this world that you want than if they don't. So my recommendation is to start building your personal brand and start building your network, even if you don't need it today. If you're like, "Ah, that seems useless. That seems like a lot of work. It seems like being awkward and putting myself in difficult situations. I'll wait till I actually need it," if you wait until you actually need it, you've waited too long and it's too late. So you need to start building it today. So one really easy way to start building it is to just update your LinkedIn, make sure it reflects everything that's going on in your life right now, and to start leaving comments on LinkedIn posts and, if you're feeling really brave, to actually start making posts on LinkedIn. That's what we do with all of my bootcamp students. And it's awkward, it's confusing, it feels weird, but I promise it's worth it in the end, and it will give you so much an advantage in your career. At this point, I have the best job on planet Earth. I'm just a data career mentor. I help my students land their first data job. But let's just say that all of that burned to the ground tomorrow. I feel pretty confident I could get a data job pretty quickly, um, because of the network I've grown. And you're like, "Oh yeah, Avery, well, you're a YouTuber, uh, 70,000 subscribers, and you have LinkedIn followers, like 150,000." Well, yeah, but at one point, in fact, five years ago, I had zero. I had none of that. And so yes, little things have built up over the last five years. But you don't have to build a YouTube channel to 70,000 subscribers. You don't have to build your LinkedIn following to 150,000. Like, just get double the connections you have on LinkedIn right now or just, you know, make one LinkedIn post. You can start small. You don't have to start big. All right, number seven. This is something that I didn't think I realized, and I don't think most people who are getting into data realize, and that is that you're going to be learning on the job Constantly. Data is constantly changing. There's constant updates, and you need to be learning on the job. It's not like accounting, where it's like... I guess accounting just took a stray, I guess. But I guess they do learn new things 'cause there's, like, new tax codes and stuff. But it's like the P&L has been the P&L, the same P&L for how many years now? It's like, it's like a very regimented way of doing things. In data analytics, like, it's just constantly changing. There's constantly new tools. There's constantly new ways to analyze things. There's constant breakthroughs, new technologies, and it's just, like, impossible to have known it all 'cause it literally changes probably every other year. So just know that you're gonna be learning on the job, and that's 100% okay. That's 100% expected. A lot of jobs, in fact, every job I've ever had, has given me the opportunity to learn on the job and given me time to actually get paid to learn. I think that is the best way to learn data analytics, is to get paid to learn. You can learn for free or you can pay to learn. The best is to get paid to learn, and you might need to learn for free or pay to learn to eventually get to that stage. But the faster you get to that stage, the, the easier, the more time you're gonna have to learn, and the better learning it's going to be, and you're making money while doing it. So that seems like a win-win-win to me, but just know that you will learn on the job. No matter who you are, no matter where you're from, no matter what the job is, you will be learning on the job. It's just, that's just the data world that we live in. Number eight, being a data analyst is more collaborative and more of a team effort than you realize. Uh, when I worked at ExxonMobil, I almost exclusively worked in pairs. Like- I would always do my analysis with someone else there, and we'd kind of do it together. Because a lot of it is actually thinking. Especially now with AI, the actual doing isn't ne- necessarily as important as it has been historically. Um, but, like, actually thinking through, are we accessing the right data? Are we doing the right metric? Are we presenting the data in the right way? So I actually did most of my analysis with, uh, another, another data person at Exxon. But then also go back to what I said, uh, earlier, which I think was number four, where you're talking to the stakeholders constantly, at the beginning and at the end especially, but also in the middle. So, like, you will be analyzing data on your own, but you'll be presenting that constantly to someone else. Um, I remember when I worked at a really small biotech startup, go make my graph, show it to my boss. "What do you think? Change this, change this, change this." Um, so it is, like, a very collaborative team effort. It's not as solo as you probably think it is. That being said, there are some roles that are going to be a little bit more solo. But from my experience and a lot of my students' experimen- uh, experience, it is kind of like a team collaborative effort. Number nine, landing a remote job is a lot harder than you think, and I just hate to be the bearer of bad news. I would love to be the person, you know, in the podcast world, if you're listening on audio, or in the YouTube world, if you're watching on video, who makes, like, a cool clickbait thumbnail, and I've made clickbait thumbnails before. I'm not saying I don't make clickbait thumbnails. But I'd love to make a really cool, uh, YouTube thumbnail where it's like, "Get a remote data job. Woo-hoo. It's easy. It's so much fun." But here's the harsh truth that, like, all the data jobs out there in the United States, probably about 14% are remote. That means there's, what, 86% that are either hybrid or in person. And let me know if I'm wrong in the comments on Spotify or on YouTube. Tell me if you want a remote job or not. In the comments say, "I want a remote," or you say, "I want it in person." And, uh, there's gonna be a lot... Prove me wrong, but there's gonna be a lot more people who want to work remotely. Everyone wants to work remotely, but there's only 14% of opportunities to work remotely. It makes it hard to land a remote job. Now, let me also tell you, when you have a remote job, there's lots of pros, obviously. Like, we all- I love working from home. It's great. But there's some cons that you're probably not thinking of, and one of them is getting training. It's a lot harder to train people via, like, Zoom. And two is career growth. I think, once again, if we go back to, what number was it? The networking one where I men- mentioned earlier. Oh, yeah, personal brand and networking really matter. It is easier to have a personal brand and network In your company, when you're in the office in person and people know your face, they shake your hand, they get to hear your jokes, your career will grow more if you are in the office than if you are remote. That's just the trade-off. And if you're, if you're like, "Okay, I don't really care about career growth, I just don't wanna commute," great. That's fine. But I just wanna let you know that remote isn't as cool as you maybe think it is, or everyone hypes it up on the internet. And it's hard to get. I just wanna be realistic with you. I, I would love to tell you it's easy and it's awesome, but it's hard, and there's some downsides. All right, number 10, it's that the imposter syndrome that you're feeling right now as an aspiring data analyst never freaking goes away. It never does. It is so hard to actually feel like you know anything in the data fields because one, it's constantly changing, two, it's immensely vast, uh, and it's, like, impossible to know everything. So that feeling you have right now that you're not good enough, that you don't know everything you should, that you don't know everything in Excel, that you've never even touched Python, that you kinda suck at SQL, guess what? That never goes away. That's just there the rest of your career. And the more... The earlier you become comfortable living in the idea of, "I don't know this, but I know I can learn this," the better. Because the data world, everything's changing literally constantly, and you will always be learning, and you'll never know it all. And so the fact that you can just own up to it and be like, "Yeah, I don't know this, I don't know that," that's okay. If I need to know that, I will in the future. If you can do those things, you will be a great data analyst