Roles in both fields are are in high demand—data scientist jobs are projected to grow 36% by 2031 compared with the 21% growth for computer scientist roles in the same timespan. What’s more, salaries for data scientists have the potential to earn six-figures or higher after program completion, while earning a master’s in computer science can earn you a $30,000 salary bump over holding just a bachelor’s degree in the field.

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“As far as commonalities between the two, both deal with some sort of computer science and programming aspects to analyze different types of data,” Jill Coleman, associate dean of the College of Sciences and Humanities and program executive director at Ball State University, tells Fortune. “That’s where the beginning part of the overlap is, that you’re learning how to do some basics of computer programming but also some basics of data visualization—you’re learning how to present those results graphically and synthesize that information.” 

While these fields do have many similarities, it’s as important to understand how they differ—and doing so will help you determine which of these two careers is the best fit for you. Here’s what experts at three universities that offer both programs told Fortune about how master’s degree programs in computer science and data science compare.

Computer science focuses on ‘the real crunchiness’ of computer programming

In the simplest form, both computer science and data science deal with extracting data, interpreting it, then presenting it to stakeholders who make decisions. The field of computer science predates data science by many decades, though it has changed with time—an evolution that Craig Gotsman has seen firsthand since the 1980s, when he was pursuing a degree in the then-emerging field.

“The field evolved primarily from electrical engineering and mathematics, and it’s all about solving problems in the digital world using computers,” Gotsman, dean of New Jersey Institute of Technology (NJIT)’s Ying Wu College of Computing, tells Fortune.

As the name suggests, the focus on “computer” continues to distinguish computer science from data science.

“You’re going tostart getting more into software design, the algorithms, the real crunchiness of computer programming,” says Coleman. “You’re also trying to learn artificial intelligence and machine learning, so you’re getting into big time computer programming and software engineering aspects.”

Data science involves drawing conclusions, making predictions based on data

Data science, on the other hand, “is a little bit more modern” and has become “a buzzword” in the past decade or so, as Coleman puts it. But the field has come into its own as more digital data has become available.

“Data science does involve some computer programming, but you’re interested in the applications of it and you start bringing math and statistics to it—merging what’s going on in computer science and statistics to create a story based on data,” says Coleman. “For example, in environmental based data, you’re looking at watersheds or atmospheric science data. In psychology and sociology, you’re looking at population, demographics, and behavioral patterns.”

The field has such broad applicability that even Coleman says she only recently realized that she’s a data scientist. “I’m an atmospheric scientist and climatologist, but I work with big data and I have to do computer programming and statistics. I put those together to apply to my field of analyzing weather patterns.”

This focus on drawing conclusions and making predictions based on data—and then communicating those findings to others—is what makes data science so unique.

“Data science is not a subset of computer science, because there are more significant intersections with mathematics and statistics,” says Simon Tavener, interim dean of Colorado State University’s College of Natural Sciences, adding that for this reason, the university’s data science program is a collaborative effort across various disciplines. “The way we constructed the data science degree at CSU was very much a collaboration between mathematics, statistics, and computer science, with input from other departments.”

Both degrees can help career switchers

If you’re debating between the two degrees in search of a career switch, it’s important to consider how your professional experience may best prepare you for one field versus the other.

“It sometimes takes people realizing they already do some of those things in their current job anyhow, but maybe in a different way,” says Coleman. “Whether or not you go the computer science or the data science route, the key word here is it’s dealing with data, right? You’re trying to sift through all those volumes of data and work for a solution.”

The skills you have already obtained will also be important to draw upon if you do apply to a master’s degree program in computer science or data science—and particularly if you don’t have a prior degree in either field.

“For example, if you’re looking for a job in civil engineering and not finding one you say, ‘Hey, wait a second, I should be studying computing and moving in that direction because there’s so much more going on there,’” says Gotsman.

Even though pursuing a master’s degree requires an investment of both time and money, doing so nets results. “Once they graduate, 95% of our students have multiple job offers within six months, and some long before they graduate,” Gotsman adds.

What experience is needed?

Previously, if a person wanted to enter one of these two fields, obtaining an undergraduate degree in a related study was considered entry-level criteria, but that’s not always the case. Many people with an educational background in an unrelated field find it’s now possible to get a master’s degree in these fields without a computer science background—and some programs are even making an effort to attract career switchers.

For example, Ball State recently redesigned its online computer science and data science programs so that applicants can gain entrance into these online master’s degree programs by successfully completing three intro courses—with a “B” average.

Similarly, the New Jersey Institute of Technology designed its master’s programs to enable people without a background in the field “to make a smooth transition,” Gotsman says, adding that the two tracks in NJIT’s data science program—computational and applied statistics—enable students to choose the specialization area which most interests them. “About 50% of our students at the master’s level are coming from outside the field,” he adds.

Finally, you should look for programs that are innovators in their respective fields and are keeping pace with the realities of today. Whether you pursue a master’s degree in computer science or data science, the program should include a component that focuses on ethics, as is the case at Colorado State, according to Tavener. “A course in ethics is crucial to both disciplines.”

Check out all of Fortune’s rankings of degree programs, and learn more about specific career paths.