Do you need a master's for a career in data science?

Data science is one of the fastest growing fields. With an expected job growth of 22 percent between 2020 and 2030 according to the Bureau of Labor Statistics, data science opportunities are popping up in business, technology, healthcare — and just about any other field you can think of.

But there aren’t nearly enough data scientists to meet the demand. And even if you have a background in computer science or statistics, it can be overwhelming to think of all the other skills you’ll need to be a good data scientist. Where do you even start?

Despite the increase in data science jobs, the field is still competitive. Companies often prefer candidates with advanced degrees in a related area, as well as relevant experience. So, if you’re looking to switch careers or get a leg up for a promotion, it’s only natural to look at higher ed options.

But do you need a master’s in order to work as a data scientist? How exactly do you break into data science as a career?

In this article, we address these questions — and share a few reasons why a Master’s in Data Science is a good option to help you build a career in data science that will keep you engaged and learning. Let’s get started.

3 Ways to Become a Data Scientist

There are three common routes people typically take to starting a data science career:

Each of these approaches offers distinct benefits and pitfalls.

1. Self-Taught via MOOCs

Everything you need to know about data science can be found is online — it’s just a matter of locating it. The self-guided MOOC approach puts the onus on you to find out what you need to learn, locate corresponding courses, and complete the courses on your own, one after another. While this approach can save you cash, you’re likely to end up with crucial gaps in your skills. that may hamper your work as a data scientist. And you may not gain the ethical grounding you need to ensure the way you work with data is sound and helpful, rather than harmful.

2. Bootcamps and Certifications

A step up from MOOCs, bootcamps and certifications provide more accountability and structure to follow through on your commitment — and you’ll likely come away with portfolio samples that demonstrate your data science skills to potential employers. However, bootcamps and certifications do not provide the breadth of knowledge that data scientists often need, and once you’ve finished, that’s usually the end of your relationship with the instructors. This path may be more appropriate for someone already working in data science who’s simply looking to close a few gaps in his or her knowledge or experience.

3. Master's Degree

Though this option can be more expensive at first glance, a master’s degree (especially in data science) will provide the depth and breadth you need to enter the data science field equipped for whatever challenges come your way. A good program will provide ethical grounding, as well as develop the technical skills you need to work with data. You’ll work on real-world projects and put data into action, while gaining an understanding of the theory that backs everything up.

So, Do You Need a Master’s to Work in Data Science?

The short answer is no. But a master’s in data science could help you advance in your data science career more quickly — without getting lost or overwhelmed by the many programming languages and tools you’ll encounter (and use!) along the way.

“A bootcamp or an online course will give you an introduction to data science,” says Kellen Sorauf, Regis assistant professor of data science, “but it’s not going to be a complete package.”

Why a Master’s in Data Science Is a Good Step for Your Career

The benefits of a master’s in data science will vary depending on the program and institution, but at Regis, data science graduates identify several program features that set them up for success, including:

1. Structure and Accountability

A well-designed data science program gives you a solid grounding in the fundamentals and is structured in a way that enables you to naturally advance your skills. Along the way, you’re held accountable via classwork, deadlines, grades, peers, and faculty – none of which you’ll find in a self-directed online course or a one-off bootcamp.

Rather than scouring the web to find out exactly what programming languages and database structures you need to learn, you can spend your energy actually learning those languages and applying theory to real-life data.

2. Grounding in Ethics & Research Practices

What ethical considerations should be top-of-mind when designing data structures and analyses? How do you make sure your data is as complete and unbiased as possible — and how do you identify and account for any identified biases so that you don’t arrive at distorted conclusions?

In our modern data-drenched environment, data scientists are on the frontline for ethical considerations in business, tech, healthcare, and countless other fields. A master’s in data science (especially at Regis) will provide the ethical and research grounding you need to make sure you’re using your skills to make the world a better, more equitable place.

3. Depth and Breadth of Learning

Want to specialize in data engineering, business intelligence, or machine learning? You can do that.

Want to gain a broad understanding of all of those concepts and how they interact? You can do that, too.

Regardless of whether you follow one of our three specialization tracts, you will be introduced to each of these areas. Our program enables you to go both deep and wide in data science, so you have the understanding to navigate whatever data science challenges you encounter in your career.

4. Portfolio-Building Projects

Learning theory isn’t enough — you need a master’s program that will immerse you in the skills and tools you’ll use every day as a data scientist. At Regis, we accomplish this through project-based learning and practicum projects. Our program has established relationships with Denver-area startups that data science students collaborate with on existing projects. We also contribute to research projects in the schools of physical therapy and pharmacy.

Our program is designed for you to practice what you’re learning and put data science into action. Along the way, you complete projects for your portfolio that show potential employers your data science expertise.

5. Faculty Feedback

Have a database question? Struggling to learn Python or SQL? Need advice for the next phase of your practicum project?

Regis faculty love teaching and they’re here to help. On top of small class sizes (your professors will know you by name), you’ll be able to interact with professors one-on-one and get the feedback you need to take your learning to the next level.

As an extra bonus: Our professors aren’t just theoretical experts — they have real-world experience working in data science. They know the subject matter inside-out.

6. Networking

Join a master’s program and you automatically become part of a larger university community. Current students, classmates, and past alumni make up a network that can help you get your first data science job and keep advancing your career. “Your Rolodex automatically increases significantly when you graduate with a degree from here,” Sorauf said.

And it’s not just about jobs! The Regis network can also help you find friendship — on campus and beyond. You’ll have data science colleagues who know exactly what it took to earn your master’s, because they were right next to you in class. Good luck finding that through a series of MOOCs.

Ready to learn more about our Master’s in Data Science? Visit the program page.