219: I Analyzed 11,000 Data Jobs to See What Skills Actually Get You Hired

When I was first starting out in data analytics, I felt extremely confused about what skills I should be focusing. And honestly, I wasted a lot of hours learning the wrong ones, and I don't want that to happen to you. Everyone has different opinions on what they think are the right skills, but what does the data actually say? So I analyzed 11,060 real data job postings to find out what skills are actually most in demand and which ones are just a waste of your time. And yes, I did a similar analysis about a year and a half ago, and about 110,000 of you guys turned in, so thank you so much for supporting me, but a lot has changed since then, so I figured it was time for an update. The first of which is that many of you told me that I was too slow to actually get to the point, and thank you, I listened, so here is the data So let's go ahead and start with last year's numbers because it's important to set a baseline to see how things have changed in 2026. So in the spring of 2025, I analyzed almost 3,000 different data analyst jobs, and here's what the ranking looked like. We had Excel on top at 39%, SQL in second place at 31%, Tableau in third place at 21%, Python in fourth at 14%, and then finally Power BI in fifth at 13%, and R at the bottom at 8%. And this is the amount of times those skills or tools were listed in all of the job descriptions. Now, let's look at how these numbers have changed since then, starting with R. So last year, R was at 8%, and this year it's actually halved to 4%. I know some of you guys learned R first, especially if you had some sort of a stats or economics degree, and really it's a fine language. I really like it, especially for statistics, but the market is clearly moving away from it, so keep that in mind because the next tool that we're gonna talk about might replace literally every single tool on this list, and it wasn't even on the list last year because it is AI, and AI and large learning model skills literally didn't have much demand say two months ago. Like, there wasn't really much evidence of it being on job descriptions, and this year it's all the way up to 11% of all data job postings. So for our data set, that is over 1,000 different jobs. One in every 10 jobs have some sort of AI or LLM mentioned in the job description. And just think about that for a second. A skill that really didn't even exist 18 months ago has already passed R, AWS, Snowflake in terms of popularity and demand. Analyzing data with some sort of AI or LLM tool is only going to get more and more in demand. But the cool part is it's one of the easiest things on this entire list to start learning. Like, you literally just use language to actually do analysis. So you have to become good at prompting, and that's kind of it, and it's a little bit more nuanced than that, you know, knowing what to analyze when, and, like, what type of analysis to do, and how to actually double-check and validate the LLM's answers. Those things are really important, but you can learn them. And the cool part is they're new to everyone. Like, these are skills that we really haven't been using, even senior data analysts. So we're almost all learning it at the exact same time, and almost nobody applying has these tools listed on their resume. So right now that's a big advantage to you, and it's one of the reasons I'm trying to cover AI in my episodes, to give you the upper hand. So make sure you hit subscribe so you keep up to date on all the latest of AI in data analytics. All right. Next we have Python, and Python went from 14% to 20%. So if you've ever been thinking, "Oh, should I learn Python or should I learn R?" Well, just look at this chart. The debate is kind of over. If you're picking one scripting language to learn from scratch in 2026, it's probably gotta be Python, unless you're going to be doing some sort of specialized government contract work or pure statistics or biology or pharmaceuticals or something like that. But otherwise, you're going to be choosing Python. I think Python is the scripting language to learn right now Next up, we have Tableau, and Tableau is up slightly from 21% to 24%. It's still a great in-demand business intelligence tool. And just keep track of this number for one second because the next tool right above it is something I kind of need to come clean about and admit to you because last year, Power BI was near the bottom at 13%. And I literally told you in the video, if you think Power BI is more common than Tableau, well, then argue with me in the comments because that's not the case according to the data. Well, according to the data this year, I was wrong last year. Power BI somehow has doubled from 13% to 26%, meaning one in every four data analyst jobs mention Power BI, and it just surpassed Tableau. My read on why, it's probably because Microsoft Power BI is bundled into the stack that most companies already pay for, like their 365 subscription or everything. So it's just, like, free, and Tableau's kind of expensive. Plus, Power BI is doing a pretty decent job of integrating AI, more than Tableau for sure. And I still think learning Tableau is really valid because who knows? Like, next year, Tableau might be slightly more popular than Power BI. You never know. And you still can't really use Power BI on a Mac, and the free version's super confusing. So I personally, like, don't really give a whole lot of credence to just 2% more popular. I still think Tableau's a little bit easier to get started with. All right, moving on to number two, and it is SQL, which is moving up from 31% to 38% of listings. And SQL is really the backbone of basically every data job that exists. Data analysts, data scientists, data engineers, they all use SQL, and it's a great tool, and it's not going anywhere at all. And the good news here is it's not super hard to learn, which actually brings us to something that's super easy to learn, and that is number one, which is Excel. And Excel is now at 49%, when it was at 39% last year. It is by far the analytics tool king. It didn't only just hold the top spot. It grew more popular and pulled even further away from SQL in second place. Basically, every other data job, one in two data jobs literally list Excel. So it's maybe boring, it may be old, but it's getting more important, not less important. Spreadsheets have been around for 50-plus years, and they've survived that long for a reason. I think they're going to be part of our future, even with AI and all the other things that are coming out. And now, since I was able to actually build out the data pipeline of getting all these jobs from my own job board, findadatajob.com, and do a little bit better analysis than I was 18 months ago, we actually also included a bunch of other things that we're tracking now, things like Snowflake, DBT, SAS. And I don't really talk about these for a specific reason. There's really not in that demand for most entry-level and intermediate data jobs. But is... Here's the numbers if you're curious. You have AWS at 8%, Snowflake at 6%, Azure at 5%, Looker at 5%. There's R all the way down there at 4%, followed by SAS at 4%, Databricks at 3%, and Google Analytics at 3%. And if you're listening audio only and you're like, "I can't see any of these charts," well, you can actually pause the episode and go to dataanalystskills- .com to see basically these exact charts that I'm showing for the audio audience. So even with all that data and all that information, what should you actually be focusing? And honestly, let's make it dead simple. All those other opinions you've heard from Reddit, from your buddies, from random YouTubers and podcasters, in my opinion, here is the optimal order backed by data. Start with Excel. It's literally the most in-demand data tool that there is, and it's also the easiest to learn. Then move to a business intelligence tool like Power BI or Tableau. They're both highly in demand and pretty easy to learn, drag and drop, clicking type stuff. But just choose one. Don't try to do both of them at the same time. They're basically the same, and once you master one, picking up the other will be fairly easy. Next, learn SQL. It's obviously a little bit harder than Excel, Power BI, or Tableau, but it's very in demand, and it's much easier than a scripting language like Python or R. Speaking of which, I recommend that you skip both when you're trying to land your first data job. Hot take, I know. They both have a steep learning curve, and they're really not all that in demand right now, so just skip them right now. Finally, don't forget to start playing with AI tools because personally, even though they're only at eleven percent right now, I think down the road, that's going to probably double by the end of the year and be twenty percent, and who knows what the next year will bring. Now, you might be listening and being like, "Ah, Avery, how do you actually know all this?" Like, "Where do you actually get this data? Is it valid? Can I trust this data?" And the truth is, I really got this data from the real world because a couple of years ago, I launched my free data job board called finddat job.com, which is where you can find data jobs. I mean, an original name, I know. And I set it up where I literally analyze the keywords, the tools mentioned in each one of the different job descriptions for every job that we post on our job board. And that's where I got these real percentages instead of just kinda like my meager opinions. And keep in mind that I might consider a data analyst job different from what you may or someone else may. Really, I dump the whole data analyst job in the data job family. So I lump in financial analyst roles, business analyst roles, healthcare analyst roles, etc. I don't include data scientist roles or data engineer roles because those are different enough. But basically, any sort of data analyst role, despite the many names for data analyst, will be included in this data set. And based off that knowledge, you might be thinking, "Avery, that's dumb. I don't like the way that you did that." And in fact, I got several comments that basically just said the same thing from my last episode. And my reply was, "Okay, great. That's fine. Go out there and do your own analysis and let me know what you find." None of those commenters took me up on that. But guess what? Now you can take me up on that because I made it easier for you to do it. So you can actually go to dataanalystskills.com, which gives you the ability to Look at this data set in a couple different bar chart ways and split it by a couple different filters. For instance, different job families. Like, maybe you just want to see what's the most in-demand role for a healthcare analyst. You can look that up. Uh, maybe you want to see, like, oh, this is for all data analyst experience levels, but what about senior data analysts? Well, that's available at dataanalystskills.com. You can even do it by, oh, what about remote versus in person, or different locations inside the United States? That way you can see the stats for whatever subset or filters you're interested in. Plus, that actually has live data that updates every day, so if anything changes from now until, you know, who knows when, you'll be able to see those live changes on the website. So please make sure to bookmark it right now, dataanalystskills.com, which is hosted on my personal job board for finding data jobs, findadatajob.com. I hope both will help you find your next data job