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

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When I was first starting out in data

analytics, I felt extremely confused

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about what skills I should be focusing.

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And honestly, I wasted a lot of

hours learning the wrong ones, and

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I don't want that to happen to you.

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Everyone has different opinions on

what they think are the right skills,

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but what does the data actually say?

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So I analyzed 11,060 real data job

postings to find out what skills are

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actually most in demand and which

ones are just a waste of your time.

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And yes, I did a similar analysis

about a year and a half ago, and about

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110,000 of you guys turned in, so

thank you so much for supporting me,

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but a lot has changed since then, so

I figured it was time for an update.

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The first of which is that many of you

told me that I was too slow to actually

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get to the point, and thank you, I

listened, so here is the data So let's go

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ahead and start with last year's numbers

because it's important to set a baseline

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to see how things have changed in 2026.

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So in the spring of 2025, I analyzed

almost 3,000 different data analyst jobs,

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and here's what the ranking looked like.

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We had Excel on top at 39%, SQL in

second place at 31%, Tableau in third

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place at 21%, Python in fourth at 14%,

and then finally Power BI in fifth

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at 13%, and R at the bottom at 8%.

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And this is the amount of times

those skills or tools were listed

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in all of the job descriptions.

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Now, let's look at how these numbers

have changed since then, starting with R.

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So last year, R was at 8%, and this

year it's actually halved to 4%.

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I know some of you guys learned R

first, especially if you had some

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sort of a stats or economics degree,

and really it's a fine language.

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I really like it, especially for

statistics, but the market is clearly

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moving away from it, so keep that in

mind because the next tool that we're

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gonna talk about might replace literally

every single tool on this list, and

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it wasn't even on the list last year

because it is AI, and AI and large

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learning model skills literally didn't

have much demand say two months ago.

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Like, there wasn't really much evidence

of it being on job descriptions,

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and this year it's all the way up

to 11% of all data job postings.

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So for our data set, that is

over 1,000 different jobs.

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One in every 10 jobs have some sort of AI

or LLM mentioned in the job description.

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And just think about that for a second.

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A skill that really didn't even

exist 18 months ago has already

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passed R, AWS, Snowflake in

terms of popularity and demand.

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Analyzing data with some sort

of AI or LLM tool is only going

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to get more and more in demand.

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But the cool part is it's one

of the easiest things on this

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entire list to start learning.

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Like, you literally just use

language to actually do analysis.

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So you have to become good at prompting,

and that's kind of it, and it's a

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little bit more nuanced than that, you

know, knowing what to analyze when,

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and, like, what type of analysis to

do, and how to actually double-check

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and validate the LLM's answers.

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Those things are really

important, but you can learn them.

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And the cool part is

they're new to everyone.

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Like, these are skills that

we really haven't been using,

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even senior data analysts.

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So we're almost all learning

it at the exact same time, and

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almost nobody applying has these

tools listed on their resume.

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So right now that's a big advantage

to you, and it's one of the reasons

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I'm trying to cover AI in my

episodes, to give you the upper hand.

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So make sure you hit subscribe

so you keep up to date on all the

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latest of AI in data analytics.

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All right.

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Next we have Python, and

Python went from 14% to 20%.

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So if you've ever been

thinking, "Oh, should I learn

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Python or should I learn R?"

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Well, just look at this chart.

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The debate is kind of over.

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If you're picking one scripting language

to learn from scratch in: 2026

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probably gotta be Python, unless

you're going to be doing some sort

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of specialized government contract

work or pure statistics or biology or

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pharmaceuticals or something like that.

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But otherwise, you're going

to be choosing Python.

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I think Python is the scripting

language to learn right now Next

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up, we have Tableau, and Tableau

is up slightly from 21% to 24%.

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It's still a great in-demand

business intelligence tool.

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And just keep track of this number for one

second because the next tool right above

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it is something I kind of need to come

clean about and admit to you because last

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year, Power BI was near the bottom at 13%.

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And I literally told you in the video,

if you think Power BI is more common

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than Tableau, well, then argue with

me in the comments because that's

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not the case according to the data.

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Well, according to the data this

year, I was wrong last year.

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Power BI somehow has doubled from

13% to 26%, meaning one in every

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four data analyst jobs mention Power

BI, and it just surpassed Tableau.

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My read on why, it's probably because

Microsoft Power BI is bundled into the

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stack that most companies already pay for,

like their 365 subscription or everything.

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So it's just, like, free, and

Tableau's kind of expensive.

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Plus, Power BI is doing a pretty

decent job of integrating AI,

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more than Tableau for sure.

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And I still think learning Tableau

is really valid because who knows?

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Like, next year, Tableau might be

slightly more popular than Power BI.

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You never know.

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And you still can't really

use Power BI on a Mac, and the

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free version's super confusing.

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So I personally, like, don't

really give a whole lot of

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credence to just 2% more popular.

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I still think Tableau's a little

bit easier to get started with.

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All right, moving on to number

two, and it is SQL, which is moving

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up from 31% to 38% of listings.

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And SQL is really the backbone of

basically every data job that exists.

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Data analysts, data scientists,

data engineers, they all use

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SQL, and it's a great tool, and

it's not going anywhere at all.

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And the good news here is it's not super

hard to learn, which actually brings us

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to something that's super easy to learn,

and that is number one, which is Excel.

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And Excel is now at 49%,

when it was at 39% last year.

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It is by far the analytics tool king.

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It didn't only just hold the top spot.

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It grew more popular and pulled even

further away from SQL in second place.

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Basically, every other data job, one

in two data jobs literally list Excel.

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So it's maybe boring, it may

be old, but it's getting more

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important, not less important.

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Spreadsheets have been around

for 50-plus years, and they've

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survived that long for a reason.

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I think they're going to be part of

our future, even with AI and all the

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other things that are coming out.

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And now, since I was able to

actually build out the data pipeline

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of getting all these jobs from my

own job board, findadatajob.com,

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and do a little bit better analysis

than I was 18 months ago, we actually

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also included a bunch of other

things that we're tracking now,

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things like Snowflake, DBT, SAS.

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And I don't really talk about

these for a specific reason.

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There's really not in that demand for most

entry-level and intermediate data jobs.

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But is…

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Here's the numbers if you're curious.

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You have AWS at 8%, Snowflake at

6%, Azure at 5%, Looker at 5%.

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There's R all the way down there at

4%, followed by SAS at 4%, Databricks

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at 3%, and Google Analytics at 3%.

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And if you're listening audio only and

you're like, "I can't see any of these

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charts," well, you can actually pause the

episode and go to dataanalystskills- .com

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to see basically these exact charts

that I'm showing for the audio audience.

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So even with all that data

and all that information, what

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should you actually be focusing?

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And honestly, let's make it dead simple.

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All those other opinions you've heard from

Reddit, from your buddies, from random

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YouTubers and podcasters, in my opinion,

here is the optimal order backed by data.

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Start with Excel.

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It's literally the most in-demand

data tool that there is, and

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it's also the easiest to learn.

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Then move to a business intelligence

tool like Power BI or Tableau.

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They're both highly in demand

and pretty easy to learn, drag

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and drop, clicking type stuff.

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But just choose one.

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Don't try to do both of

them at the same time.

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They're basically the same, and

once you master one, picking up

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the other will be fairly easy.

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Next, learn SQL.

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It's obviously a little bit harder than

Excel, Power BI, or Tableau, but it's

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very in demand, and it's much easier than

a scripting language like Python or R.

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Speaking of which, I recommend

that you skip both when you're

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trying to land your first data job.

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Hot take, I know.

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They both have a steep learning curve,

and they're really not all that in demand

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right now, so just skip them right now.

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Finally, don't forget to start playing

with AI tools because personally, even

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though they're only at eleven percent

right now, I think down the road, that's

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going to probably double by the end of

the year and be twenty percent, and who

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knows what the next year will bring.

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Now, you might be listening and

being like, "Ah, Avery, how do

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you actually know all this?"

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Like, "Where do you

actually get this data?

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Is it valid?

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Can I trust this data?"

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And the truth is, I really got this

data from the real world because a

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couple of years ago, I launched my free

data job board called finddat job.com,

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which is where you can find data jobs.

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I mean, an original name, I know.

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And I set it up where I literally analyze

the keywords, the tools mentioned in each

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one of the different job descriptions for

every job that we post on our job board.

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And that's where I got these

real percentages instead of just

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kinda like my meager opinions.

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And keep in mind that I might consider

a data analyst job different from

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what you may or someone else may.

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Really, I dump the whole data

analyst job in the data job family.

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So I lump in financial analyst

roles, business analyst roles,

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healthcare analyst roles, etc.

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I don't include data scientist

roles or data engineer roles

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because those are different enough.

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But basically, any sort of data

analyst role, despite the many

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names for data analyst, will

be included in this data set.

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And based off that knowledge, you

might be thinking, "Avery, that's dumb.

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I don't like the way that you did that."

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And in fact, I got several comments

that basically just said the

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same thing from my last episode.

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And my reply was, "Okay, great.

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That's fine.

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Go out there and do your own analysis

and let me know what you find."

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None of those commenters

took me up on that.

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But guess what?

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Now you can take me up on that because

I made it easier for you to do it.

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So you can actually go

to dataanalystskills.com,

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which gives you the ability to

Look at this data set in a couple

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different bar chart ways and split

it by a couple different filters.

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For instance, different job families.

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Like, maybe you just want to

see what's the most in-demand

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role for a healthcare analyst.

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You can look that up.

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Uh, maybe you want to see, like, oh, this

is for all data analyst experience levels,

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but what about senior data analysts?

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Well, that's available

at dataanalystskills.com.

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You can even do it by, oh, what about

remote versus in person, or different

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locations inside the United States?

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That way you can see the

stats for whatever subset or

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filters you're interested in.

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Plus, that actually has live data

that updates every day, so if anything

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changes from now until, you know,

who knows when, you'll be able to see

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those live changes on the website.

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So please make sure to bookmark it

right now, dataanalystskills.com,

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which is hosted on my personal job board

for finding data jobs, findadatajob.com.

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I hope both will help you

find your next data job