_{I analyzed 101 current Data Science job postings.}

Here are the 5 most in-demand Data Science skills.

TL;DR

If you are reading only one thing today, here is the takeaway.

• To become a Data Scientist in 2025, focus on these 3 technical skills first: Python, Machine Learning & SQL.

• Next, wrap these 3 tech skills in solid Product & Business sense — and you’re golden.

This is my second year analyzing open Data Science roles, to find the most in-demand skills. Last year, I analyzed 100 open roles, but this year I analyzed 101 roles. Impressive progress, I know.

In this article, I am only reporting on the top required skills, but I also have information on industry, salaries and roles. All in the context of Data Science, of course. Would you like to hear more about current Data Science industry trends? Vote below! 

Before we dive into it, here are the parameters of my data collection process:

• Country: US roles only

• Role titles: Anything that contains “Data Scientist”

• Role types: Full-time, individual contributor (i.e. non-manager)

So, without further ado, here are the top 5 skills required of Data Scientists in 2025… <drumroll please>

#5: R

R the programming language came in 5th place with 50% of companies asking for it.

However, almost every job posting that mentioned R, also mentioned Python. i.e. You could have either of those skills to be considered “qualified” for the jobs.

But spoiler alert: Python takes the number 1 spot as most desired Data Science skill in 2025. So if you’re just starting your programming journey, I recommend learning Python over R.

#4: Product sense & business sense

More than half (55%) of the job postings required business or product sense, but this is also the hardest skill to build.

Here’s what I did to build product sense & business sense — that eventually landed me roles at Meta and Google etc.

1. Reading the book, Cracking the PM interview. I know this sounds tangential to being a Data Scientist. But, Product Managers know products & business really well… so learn from the best!

2. Reading Tech blogs to learn how companies make decisions with data. Personally, I really like reading Netflix’s tech blog.

3. Build projects that solve real-world problems. Find a real world problem and a real-world dataset. Now imagine you’re the Data Scientist on that problem — what would you do?

#3 SQL

SQL is the second most in-demand coding language for Data Scientists. 62% of Data Science jobs require SQL.

SQL is easier to pick up than Python. My advice if you’re just learning to code, start with SQL — it has a smaller learning curve and a more focused scope.

Some people ask if SQL is going away, because “AI can now do that work”. But, I disagree… strongly disagree. I don’t think AI is able to fully replace SQL querying since (for now) it still struggles with:

• Integrating business context into a query

• Writing complex SQL queries

Echoing advice from the last section: when learning SQL, make sure you’re thinking about how to use it in a real business setting.

If you’re looking for hands-on practice, where you can apply SQL to real business problems. Check out www.InterviewMaster.AI to get started for free. 

#2 Machine Learning

Machine Learning took the number 2 spot this year — 65% of Data Science jobs require it. It’s no surprise given the recent advancements in AI. Virtually every company wants to implement an “AI solution”. Even if it’s just so they can say they did it. But I digress…

But, almost all of these roles mentioned Machine Learning generically. They say vague things like “Experience with machine learning frameworks”.

Or said another way, there was little mention of Advanced ML techniques. 

What does this mean for you? Focus on the basics of Statistics and your fundamental Machine Learning concepts. Make sure you really understand the basics, before moving on to more complex topics like Deep Learning, Computer Vision, NLP etc.

#1 Python

With Machine Learning taking up the #2 spot. It’s no surprise that Python came in at number 1, with 86% of jobs asking for it.

You might be wondering, “Do I have to know both SQL and Python?”

Yes, absolutely! SQL is typically how you extract & transform data from a database. Python is where you do deeper analyses and build models.

If you’re learning Python, make sure you’re learning Python for Data Science. The libraries that you specialize in will be different from software developers. By the way, I put together a Python learning roadmap for Data Scientists, that you can download here — for free, and no strings attached.

In case you missed it…

I launched a SQL interview preparation platform — www.InterviewMaster.ai!! 

My cofounder, Jeremiah, and I built this to help people ace their SQL interviews & land their dream jobs. Over the past 12 months, we’ve been talking to Data job seekers at all levels and from all industries.

We identified some common struggles related to SQL interviews, and Interview Master is our solution to solve for these problems:

• Questions are based on real companies & real products… So you are ready for real-world interview questions

• Real-time & customized feedback on your queries… So you can get better at SQL — fast

• AI chat that answers your questions & gives hints … So you don’t have to toggle between ChatGPT and Google

• AI Interviewer that mimics a real-life interview… So you are confident going to actual interviews

Hi there! It’s been a while since I’ve sent out a newsletter article. My goal is to start sending out bi-weekly newsletters again in 2025. If you are no longer interested in receiving newsletters from Ask Data Dawn, feel free to unsubscribe at the link below. I will be sad to see you leave, but I also get it… we all get way too many emails :)