119: Think Twice Before Switching to Data Analytics

[00:00:00] Welcome to the Data Career Podcast, the podcast that helps aspiring data professionals land their next data job. Here's your host, Avery Smith. Hey everyone. Welcome back to the Data Career Podcast. I'm your host, Avery Smith. I'm stoked that you guys are listening to this episode.

[00:00:42] It is a really cool episode where I took on listener questions, question from you guys, and I answered them one by one in what I call an Ask Avery session. So it's kind of a ragtag multiple of questions here about how to start your

[00:00:57] data career and what you should be doing in that process. So if you're in that journey, then this episode is for you. I just want to give a quick plug to my newsletter.

[00:01:07] If you guys aren't on my email list yet, you have to be because I'm sending out weekly data analyst tips and tricks on landing your data career faster. I'm sharing success stories. I'm sharing how to use LinkedIn. I'm sharing how to do networking.

[00:01:19] I'm sharing ideas for projects, and I'm sometimes even giving job opportunities in there as well. So if you guys aren't signed up yet, use the link in the show notes down below to sign up for that. Let's get into it. I'm starting a career in data analytics.

[00:01:32] Where do I start from? So if you're starting to transition your career in data analytics, where do you start? The answer is start with what experience you already have. So the majority of people who transferring into a data analytics career have already

[00:01:45] used Excel at some point in their career. So an Excel is a really easy place to start because you're already familiar. I would tell you to learn the basic things like sorting and filtering in Excel, right? Doing things like formulas, aggregations, mostly some, some if count min max

[00:02:03] average, those types of things in Excel. And then learn how to do an X lookup and then learn how to do a pivot table. If you can do all those things in Excel, you're in a pretty good place.

[00:02:13] So that's where I'd tell you to start is start in Excel. Peter wants to know how can I start networking as an aspiring data analyst? Here's one quick way that you can start networking really easily.

[00:02:26] Open up your phone, go to your contacts, go through your contacts one bit at a time and write down where everyone in your contacts works. Now ask yourself, would this company hire a data analyst? Yes or no. Do they have data analysts on staff already? Yes or no.

[00:02:42] Answer that for all of your contacts. And then for the companies that have data analysts on staff, text your friends and say, Hey, do you like working for this company? I want to become a data analyst. I think your company has data analysts.

[00:02:54] Do you know any data analysts at that company? That's it. You're just saying, do you like this company? Do you know any data analysts? That's a super simple text. If they're actually your friends, even if they're just like, honestly, someone

[00:03:04] you're acquaintance with, they'll probably be willing to help you. This is a great way that you can open the door of the data world and network without having to do anything that difficult. Okay. So I know all of you guys can go home and do that tonight.

[00:03:16] That's one quick tip that I call the phone book method that you can actually start networking really easily without putting in honestly any effort whatsoever. What data analyst positions would you recommend for a truck driver? Now, I think that's totally awesome.

[00:03:30] If you want to go from some sort of a job like truck driving or logistics into data analytics, and there's definitely a place for you. In my opinion, a supply chain analyst or a transportation analyst would probably be the top two places I would look.

[00:03:43] The reason is because when you're breaking into data analytics, you always want to rely on the past experience that you have and mix it with the data analytics future you want. In truck driving, fleet management, you have a ton of logistics and supply chain

[00:03:56] and transportation knowledge that I don't have, right? And so that would help you land a job over me, even though maybe I have more data experience than you. You have a domain background, which is very valuable.

[00:04:06] So in those roles, a lot of the times you're going to have to use one or multiple of the big three being Excel, SQL, and some sort of BI tool like Tableau or Power BI. And those things you could honestly learn in probably about a month.

[00:04:22] In my bootcamp, the Data Analytics Accelerator, we'll teach you Excel, we'll teach you Tableau, and we will teach you SQL as well. This is from AcceptableCapital77. They posted on the subreddit data analysis and they said, I spent eight

[00:04:37] months of my life trying and failing to become a data analyst and I've finally given up on it. After getting laid off in November of last year, I decided to make a career change and become a data analyst.

[00:04:47] I had no experience and knew I'd have to learn a lot of skills to get my foot in the door. I had a good amount of savings, so I decided to study full time, enrolled in a data analytics bootcamp and spent all my time outside of class

[00:05:00] reinforcing what I'd learned. I got really good with Python, Excel, VBA, SQL, and Tableau, and had a good understanding of the data analysis process after around six to seven months of studying full time. I graduated the course and continued doing personal projects and started

[00:05:16] applying for data jobs, which I was optimistic about at first, but quickly realized literally nobody wants to hire an entry-level data analyst right now. I applied for jobs all day, every day for almost three months, set out 312

[00:05:30] tailored resumes with my skills and projects from my GitHub, and got a couple interviews, but nothing came from them. After three months of sending applications into the void, I finally gave up and last week just accepted a job offer doing what I was doing

[00:05:43] before I got laid off as I had plenty of experience of that on my resume. It really sucks to have spent so much time and effort learning these things that I will probably never use now, but I'm really not sure what I could have done differently.

[00:05:55] I have the skills, but just never got a chance to prove it. Just couldn't get my foot in the door. I guess this is a warning that data analysis is really oversaturated right now. So if you're thinking of a career change and have no experience or

[00:06:07] connections in data analysis, I just want to warn you of my experience. So first off, Acceptable Capital 77, I'm super sorry. That sucks. And I wouldn't wish that upon anyone. It's never fun to spend that long on something you're really looking forward

[00:06:21] to and excited about, and then it just doesn't pan out. That's not fun at all. But there's a couple of things I think that you could have done differently personally. So the first thing that I'm reading when you went through this whole thing right

[00:06:33] here, is they started their skills with Python and VBA. You guys don't learn VBA. VBA is a dying language. If you don't know what VBA stands for, it stands for visual basic for applications. It's basically programming inside of Excel.

[00:06:50] I'm not talking about like the functions or the formulas or the equations that you are familiar with. It's like a scripting language inside of Excel. Don't learn it. It's dying. It's not going to be asked for that often on the job.

[00:07:02] And most companies in the next five years will pivot from it to something like JavaScript or Python. So it's honestly just a dying language. I wouldn't worry about it. Second off, I love Python. Python is my favorite data tool, but I don't think you should start by learning

[00:07:17] it, especially when you have no prior experience. There is such a steep learning curve to Python. And I know so many people who have awesome jobs, who have six figure jobs, who can't code one line in Python. It is just so much to learn.

[00:07:29] And it's honestly difficult to learn. I'm not going to lie to you. Learning how to program is difficult. Python is difficult. I wouldn't learn it. SQL and Tableau, I would keep. I think those are two good things that you should be learning right there.

[00:07:42] One of the things that I also noticed here is they mentioned after, yeah, I had a good understanding of data analysis. I had a good understanding of the data analysis process after around six to seven months of studying full time.

[00:07:56] Then I applied for jobs all day, every day for almost three months. You guys don't wait six months to apply to your first data job. You need to be applying to your first data job honestly within eight weeks of starting the process. And I know that feels counterproductive.

[00:08:11] It's like, well, this person was a worst candidate at the beginning. Why should they be applying? And the reason is something that Ali Abdaal says in his YouTube videos when he talks about anything, when he's talking about learning anything. Productivity in general.

[00:08:23] First you get going and then you get good. Honestly, you need to send probably 30, 50, 75 applications just to figure out your process. Just to try to figure out what's needed, what the job descriptions say, so on and so forth.

[00:08:38] Because if you're actually studying the job descriptions, you'll find that Python is only mentioned in around like 33% of the job descriptions for data analyst jobs, so you're going to just need to get your reps in, to be honest.

[00:08:51] So I would not wait to apply to jobs after you've quote unquote learned everything, to be honest, you're never going to learn everything in this whole entire journey. So just start applying today and build off of it. Second off, I would never tailor 312 resumes.

[00:09:06] Like what in the world were you tailoring those resumes for? I know that there's advice to tailor your resumes. I don't think it's actually really worth it. So what I would do instead is probably make about five resumes based on different titles.

[00:09:18] Like for instance, a business analyst, a data analyst, a marketing analyst, and a financial analyst resume. That way you're only tailoring five resumes and based on whatever job you're applying for, you can use one of those resumes. Another thing that I noticed in this message right here is

[00:09:33] projects from my GitHub. I don't swear. I tried not to be explicit and I'm going to contain my emotions here. I hate GitHub. The idea of a GitHub portfolio for a data analyst is honestly, this is controversial, but it's honestly one of the worst things that

[00:09:49] has been said in our industry. I don't believe in it. I don't know how many people, honestly, I really don't know how many people have a GitHub portfolio and get hired as data analysts. From my opinion, I love a portfolio.

[00:10:02] I just think GitHub is terrible for a portfolio. And to be honest, if you actually like read between the lines, GitHub thinks that GitHub is terrible for portfolios. Do you know why? Because they created a new product called GitHub pages. That is separate from GitHub.

[00:10:18] I mean, you build it in GitHub, but it's a whole separate product and GitHub built it because they're like, Oh, people are using GitHub for a portfolio. But you know what GitHub's made for? It's meant for like Google docs for code.

[00:10:31] Like imagine trying to build your portfolio on like, I'm trying to think of a good way, like on like a menu for like a restaurant. It's like, okay, you can do this. That's it kind of works, but it's not what we made this for.

[00:10:46] So I see in the, in the comments here. What about Notion? Notion works great. Any sort of like website where you can actually explain what you did in depth and use pictures and use videos and just like be more descriptive is so much

[00:11:01] more powerful than just giving someone a GitHub portfolio link that were the GitHub portfolios are just so ugly. You can make a GitHub portfolio kind of pretty if you have the readme. But the readme is just like, it's hard to do well.

[00:11:14] A lot of people don't do it very well. And I just like, don't really recommend it to be perfectly honest. So I would, I wouldn't personally made my resume in GitHub. I think that was a little bit of a mistake.

[00:11:25] Some of the other things that I think that I would have done differently. So let's see. Yeah. Giving yourself like you only gave yourself three months to be applying. And honestly, you probably like, I think you can do it in three months, but

[00:11:42] honestly, six months is probably where I'd start to draw the line. And I think that's something that you can actually do if you move the job applying phase earlier in your job search. I think that is actually more useful there in terms of applying

[00:11:55] for jobs all day, every day. That sounds really miserable. I would question and I'd say, how many cold messages did you send? Sending cold messages is so key network matters so much. And I know it's not fun to hear, but like, it's not, it's not what you know.

[00:12:11] It's who, you know. Right. And so the more opportunities you give yourself to know someone, for instance, by doing that phone book method can be really, really powerful. Let's see what else this person said. They were also trying, this was posted eight months ago.

[00:12:26] So this was about the beginning of 2024. I will say that I have seen the job market and really pick up in the spring and summer of 2024. It's been better than it was in 2023. What else do I, what else? What other advice would I give this person? Yeah.

[00:12:41] I graduated the course, continued doing personal projects and started applying for jobs. Honestly, you need to start applying for jobs three months in or less. That's really key. Literally nobody wants to hire an entry-level data analyst right now. They do. It happens. I promise. I know it's frustrating.

[00:12:57] It's competitive for sure, but it happens all the time. The last thing I'd ask you acceptable capital 77 here is what is your current domain? What was your current job in? Because sometimes it is hard to like pivot to it. For instance, I was in chemical engineering.

[00:13:12] Basically it would have been hard for me to go from chemical engineering to becoming like, for instance, a financial analyst for like a FinTech company, that would have been a big jump because not only am I not only doing chemical engineering, I'm moving industries to like FinTech

[00:13:27] and then I'm doing data analysis there. So it's like a double big job. If you can simplify that and become like a data analyst, for instance, if it was me in like the chemical or the chemical engineering world, that is a smaller jump.

[00:13:38] And then once you've secured that job, you could always go to a different industry. So I hope that helps. Those are some of the things I think you could have done differently. Acceptable capital 77. If you're ever open to it, I'd love to meet you.

[00:13:49] I'd love to talk to you on Reddit and help you through. That is it for today's episode. I hope you guys enjoyed that. And if you did go ahead and sign up for my newsletter, it is in the show notes down below.

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