Is a Master's in Data Science Still Worth It? A Data Science Director Weighs In

How I Got Into AI/ML, and What My Degree Did for Me

Why the Master's Mattered at the Time

At the time I obtained my master's degree in data science prior to the AI boom, it opened the door for me to pursue several data science, analytical and managerial roles that aligned to my desired career path and of course had better pay. Many of those positions at the time required a master's degree to even apply, so I would have had a harder time getting my foot in the door without my degree.

That's still true at some employers. The typical entry point for data scientists is a bachelor's degree, but "some employers require or prefer that applicants have a master's or doctoral degree," according to the U.S. Bureau of Labor Statistics. BLS projects data scientist jobs will grow 35% from 2025 to 2035, with a median pay of $120,230 in May 2025.

The options have multiplied since then, too. Alongside traditional data science master's degrees, there are now hundreds of online graduate programs in artificial intelligence, many of which cover the same statistics and machine learning core with a heavier emphasis on modern AI systems.

In addition, my classmates and instructors also fostered a strong networking community and connections that I would utilize during and after obtaining my degree.

What I Still Use as a Director

Aside from my degree itself, the courses I took helped me to improve my technical/programming skills and helped me to better understand the core concept of ML/AI models and applying them in business cases.

For example, training and testing models were a new concept to me. Knowing the key diagnostic measures to evaluate a model's performance or accuracy is something I still use today being in a director role, as we are currently evaluating 3rd party AI products for our business.

Which Parts of My Education Mattered Most and Least?

Learning the AI/ML Concepts

The part of my education that mattered most was really the exposure to AI/ML concepts, which gave me a fundamental understanding of how to apply them in real-world situations. This naturally pushed me to pursue these concepts in my work, where I gained the hands-on experience needed to develop predictive models.

In addition, graduate school also made me aware that I really needed to strengthen my programming skills to become a well-rounded data scientist. I realized that I needed to pursue more "data engineering" skills on my own to be able to oversee a data science project from start to finish.

Programming Skills Came From Work

While my graduate courses introduced me to technical skills such as SQL, R, and Python, these skills truly matured through hands-on practice and application in my everyday work. There's only so much programming skill that can be gained by taking a few courses. It needs to be gained through experience.

And now with AI, tools are increasingly making programming more accessible and efficient. While this is all good, I believe that understanding code, knowing how to read it, and being able to evaluate its logic remain critical skills that really weren't learned through my degree alone.

When I Hire, How Much Does a Degree Matter?

Asking me this question five years ago, I would have had a different answer than I do now. Today, I would say I don't value an ML/AI graduate degree as much as I used to; however, I still look for candidates that can showcase their core understanding of AI/ML concepts. For me, projects and experiences weigh more on my hiring decisions than just a degree by itself.

Why I Look for Data Engineering Experience

I am also looking for candidates who have solid data engineering experience. Too many times, I've seen recent data science graduates that lacked the hands-on experience working with the data directly, such as the programming skills needed to clean and transform the data and how to handle missing data, to name a few. They are also often unfamiliar working with databases, and data is the most critical item for any AI/ML project.

Why Experience Still Wins With AI Tools

Even though AI helps to fill any skill gaps, I would still heavily favor experience. Experience provides the vision to ensure AI-assisted projects or tasks are navigated successfully, and the awareness of landmines or pitfalls to avoid in the event an AI-assisted project goes off course.