In fact, one program on Fortune’s ranking of the best master’s in data science programs has a lower acceptance rate than any MBA program featured on Fortune’s rankings. The University of Michigan in Ann Arbor’s data science program, ranked No. 1, has an acceptance rate of 5.3%. 

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By comparison, the most selective MBA programs last year were Stanford University, with a 6.2% acceptance rate, followed by the Massachusetts Institute of Technology (Sloan), with a 12% acceptance rate.

Here’s why the University of Michigan’s data science program is so popular.

Application count for the program continues to grow, cohort sizes remain the same

While plans to create the program began in 2015, the first cohort of 24 master’s degree students in data science enrolled at the University of Michigan in fall 2018. The program was launched late in the application cycle, but still received considerable interest with 111 applicants. The next year, 679 prospective students applied.

In 2023, the program garnered the most applicant interest yet, with 1,555 prospective students submitting applications—and 85 accepted students are expected to begin class in the fall. And the program doesn’t have any long-term plans to admit many more students than that, says Ambuj Tewari, director of the master’s in data science program at the University of Michigan.

“Even compared to the first full year of 2019, applications have already more than doubled,” Tewari says. “The class size in 2020 was affected by pandemic-related deferrals, but apart from that, we have slowly increased the class size in response to the explosive growth in applications.”

High demand for the degree programs offered by the University of Michigan’s department of statistics partly informed the success of the master’s in data science program. At the undergraduate level, 800 students are currently enrolled in data science or statistics majors or minors. The master’s in applied statistics program, first created in 1985, is also highly selective, enrolling about 70 students a year. 

The school already had the foundation for a rigorous graduate degree program in data science, Tewari says. The school’s existing Michigan Institute for Data Science (MIDAS), department of biostatistics, department of electrical engineering and computer science, department of statistics, and the School of Information collaborated for the creation and facilitation of the data science program. 

“This broad partnership gives our students access to a breadth of expertise and courses covering various aspects of computation, information, and statistics that is simply not available in competing programs run by just one or two units,” Tewari says.

Additionally, the fact that the program is completely in-person means students have plentiful opportunities for research experience. Faculty includes editors of top journals, fellows, and world leaders of organizations like the American Statistical Association, the Institute of Mathematical Statistics, and the International Statistical Institute.

“This is really important for a discipline such as data science which has wide-ranging applications to the natural sciences, business, law, medicine, and the social sciences,” Tewari notes. 

Accepted students demonstrate strong quantitative skills

With a sub-6% acceptance rate, the master’s in data science program is understandably reserved for high-caliber students. To get into the program, prospective students must demonstrate substantial quantitative skills.

“We do look for quantitative background—without it, it will be very difficult for admitted students to handle our rigorous coursework that blends core courses drawn from statistics and computer science with application electives,” Tewari explains. “But we do not look for a specific undergraduate major, and a large fraction of our applicants are indeed aiming to pivot into data science from another related major.”

Popular undergraduate majors in the applicant pool include computer science, mathematics, statistics, and information science—as well as less-related majors like economics, engineering, finance, neuroscience, and physics. 

“We value real-world data science experience of any kind–as part of a research project or as part of a prior job,” Tewari says. 

Recommendation letters are also considered carefully. Specifically, the admissions team looks for evidence of independence, initiative, and team spirit. 

“If an applicant has the required quantitative background and makes a strong case in their personal statement for why they want to pivot to data science, we are very happy to admit them,” Tewari says. 

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