158: I Analyzed 2,893 Data Analyst Job Postings to Find Out What Skills You ACTUALLY Need to Get Hired

[00:00:00] Avery: When I was first starting out in data analytics, I often felt overwhelmed trying to keep track of all the skills I was supposed to learn. I worked really hard to learn everything that I kept hearing about on LinkedIn and YouTube, but it only kept growing bigger and bigger. Every time I learned one skill, it seems like three more skills popped up on my list.

[00:00:19] Avery: Honestly, it made me feel like I was never going to learn at all and never going to Land's data job. Looking back now, I wasted a ton of time learning skills that turned out to not even be very relevant. To make sure that won't happen to you, I analyzed 2,893 different data analytics, job postings to find out what skills employers.

[00:00:39] Avery: Actually care about in 2025. In this video, I'll reveal what those skills are and how I came to discover this through a data-driven method. But first, if you're feeling confused, just know you're not alone. I felt that way, and I think most people watching this video feel the exact same way. If you feel that way, comment in the comments down below, and the internet doesn't make [00:01:00] things any easier.

[00:01:01] Avery: Coursera says one thing while data camp says something else. Heck, even this one single Reddit thread that goes through what you should be learning, everyone is saying something different. Some people said, learn power bi. Others said, learn sql. Some people said it's all about Python and it's honestly all over the place, and the whole thing is just so confusing.

[00:01:20] Avery: It does kinda make sense though, because there are so many different types of analyst jobs out there. There's so many different companies and there's so many different industries, so every single data job, of course, will look a little bit different. Some data analysts are literally nose down to the keyboard in Python all day every day, programming, while others never even touch Python an an entire 30 year data career.

[00:01:42] Avery: So, anecdotally, people are gonna have opinions, but they're just that they're opinions. As a data nerd, you probably care a lot more about what the numbers actually say. These opinions. So what do the numbers actually say? Well, the trick is the numbers are really hard to find because one, the data collection [00:02:00] is very difficult and there's no one really out there doing it that much.

[00:02:03] Avery: And even if you do collect the data, analyzing it proves pretty difficult as it's extremely unstructured and dirty data, it doesn't fit nicely in some rectangular table. There's no numbers that you can analyze. Just a bunch of text and the only one that was really working on this previously was Luke Perus.

[00:02:20] Avery: I'm sure you guys have seen him on YouTube before. He's been on my podcast as well, and he carried the torch for a long time. Well, I am now here to help. If you didn't know, a couple months ago I launched my own free data job board that's specifically for people trying to land data job. It's called Find data job.com.

[00:02:36] Avery: Really original name. I know. Go check it out if you haven't already, because we post some absolute banger opportunities there. And it's 100% free. Finally, after a lot of work, got it set up to now self collect and analyze its own data, which is very exciting. Here's exactly how it works. First, I built some web scrapers that basically go around the internet looking for juicy data jobs, uh, and different niches with [00:03:00] different titles.

[00:03:00] Avery: These are data jobs that you probably won't see on the big platforms like Indeed LinkedIn or ZipRecruiter, because these companies, a lot of the times, they don't want to pay the hefty fees. Those platforms often charge. I'm looking for data analyst roles, financial analyst roles, business analyst roles, and a couple others.

[00:03:17] Avery: Basically anything that involves data and is somewhat entry level, we are looking for these web scrapers. Try to find around 50 new data jobs. Every single day. Sometimes they fail and they don't find quite that much, and sometimes they succeed and find more than 50. But on average it's about 50. That data gets added into my database, which yes, is kept inside Google Sheets and with a combination of LLMs automation tools, Python and spreadsheets.

[00:03:41] Avery: We collect and analyze that data. We analyze a lot of things, and I can make a bunch more videos in the feature like this. But specifically, we count how many times keywords are used in job descriptions. For example, how many times is the word Python used in this job description? And we're able to see how often a job description requires different tools or [00:04:00] skills like Excel, sql, tableau, so on and so forth.

[00:04:02] Avery: Uh, and let's go ahead and dive into the results. These are the most in demand data skills in 2025. Starts with Excel. Excel is required. 39% of the jobs posted on find a data job.com. Next, it moves to SQL at 31% of the time, followed by Tableau at 21% of the time. Next is Python at 14% Power BI at 13% followed by R at 8%.

[00:04:27] Avery: Now, you might wanna take a second and pause to look at this, to think about this, because it honestly should probably change the way you approach the job search. For example, all those people on the Reddit thread who said, yeah, you need to be studying Python. 'cause that's what I use at the job. Well, it turns out that 86% of job descriptions don't even mention the term Python.

[00:04:46] Avery: So it's like really? Should you be learning Python when it's required only 14% of the time? Mm, probably not. Instead, you might wanna focus on the king of all data analytics tools, which is Excel. I know, kind of boring, not all that glamorous, but Excel [00:05:00] remains the number one data tool, especially when you're trying to land your first data job.

[00:05:04] Avery: So making sure that you focus and study a lot in Excel and you feel very proficient in the tool is probably the best way you can spend your time. Next. That's followed by sql, and SQL is really the backbone tool of every data analytics and data engineering and data science job. You can do it is pretty much omnipresent.

[00:05:21] Avery: It's obviously required as much as excel in these roles, uh, but it's going to be required more than excel in more advanced roles like a data engineer or a data scientist role. So it's definitely worth learning and you can honestly learn quite a bit in two weeks. You'd honestly be amazed. Third is Tableau.

[00:05:36] Avery: And I think Tableau is a great BI tool, business intelligence tool or a data visualization tool to learn it is in demand. A little bit more than Power bi to everyone's surprise. Go ahead and go to the comments if you think Power Bi. It is more common than Tableau. It's not according to the data, uh, but Tableau is also a little bit easier to get on your computer, especially if you have a Mac.

[00:05:55] Avery: So it is a great place to start and develop a data visualization tool. But hey, [00:06:00] power BI is not bad either. It's a great tool. No shade there. Next we have the scripting tools that are gonna be Python and R. As you can see, python's about double popular as r. I think that trend will continue moving forward.

[00:06:12] Avery: I personally like Python. You can do a lot more with it. There is a little bit more of a steep learning curve with learning Python than there is R, but I think if you're only gonna learn one, I would personally choose Python. Now, obviously find data, job.com is adding like 50 data jobs a day, so these numbers will change over time.

[00:06:29] Avery: I don't see them changing a lot, but they will change a little bit. So what the cool thing is, is we've actually put together a live version of this graph@dataanalystskills.com. You can go there today and see the live chart that will actually update every single day with this data. And like I said, I don't think that these numbers will change too much, but from time to time we might see some increases or fluctuations, ebbs and flows depending on how the market's going.

[00:06:53] Avery: I hope this information helped you become more data-driven in your data career journey. Honestly, it's nice to actually have [00:07:00] the data behind all of this, and these findings are gonna shape the content I create here on YouTube and in my podcast as well as inside my accelerator program. And it was actually really cool to see that inside of the accelerator we're already teaching these skills and.

[00:07:14] Avery: Pretty much in this exact same order. So if you're interested in a step-by-step path to becoming a data analyst from Absolute Scratch with the most relevant skills, real projects, and real support, then go ahead and check out the accelerator data career jumpstart.com/daa. All of the link in the show notes down below.

[00:07:31] Avery: And one of the perks of joining the accelerator is you can actually do an internship project with my company and work on real data sets just like this. So I'd like to actually thank my intern manager, Isaac or Asya, and three of my accelerator interns who worked on this project and did a great job.

[00:07:50] Avery: Austin Sorenson, Isabel Klunder, and Moise Naali for helping me clean this data set and make this graph come to life. Good job you guys. May the odds be ever in your favor. Everyone. [00:08:00] Thank you for watching. If you enjoyed this video, hit subscribe and maybe watch one more. Cheers to us being more data-driven in our data career pivots.

[00:08:07] Avery: I wish you guys all the best of luck. Thank you for listening.