To address the rising industry demand for experts who can collect, organize, and interpret large datasets, universities are increasingly creating master’s in data science—designed for students of all ages looking to gain in-demand skills. To navigate the uncharted ecosystem, Fortune has ranked the best master’s in data science for 2025. Here’s how we did it.

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Methodology for Fortune’s ranking of the best master’s in data science 

Fortune researched nearly 100 data science graduate programs, and more than 35 schools participated this year. Here is how we weighed the data to formulate our ranking:

Data that Fortune collected:

• Previous ranking/participation: 5%
• Conductor annual search volume: 5%

School-provided data:

• Total program cost, out-of-state U.S. residents; 15%
• Acceptance rate, academic year 2023-2024 (admits/applicants): 10%
• Yield, 2023-2024 (Matriculates/Admits): 10%
• Average undergraduate GPA, 2023-24 enrollees: 10%
• Graduation rate, 2021-24: 20%
• One-year retention rate, 2023-24: 25%

Our expert panel 

In conjunction with the development of our final ranking, Fortune spoke with two data science experts about the growing tech education ecosystem:

• Rayid Ghani, distinguished career professor at Carnegie Mellon University’s Heinz College of Information Systems and Public Policy.
• Karthik Ramasamy, head of streaming at Databricks.

We chatted with both to gain a better understanding of what students need to know to succeed in data science, what makes a program distinct, and what skills are most important. Neither individual was involved in the actual ranking of any programs.

The perfect MSDS: Easy to describe, hard to find

For prospective students looking for a high-paying, high-stakes career, Carnegie Mellon University Professor Rayid Ghani offers a simple but critical question: "Are you getting [a master’s degree in data science] for the credentials or the skills? If you have the skills but need credentials, then get any data science degree. If you're getting it for skills, find one that augments your existing skillset."

An ideal program should blend working with real-world problems and access to experienced instructors—but the degree might not be for everyone. "You don’t need a master’s degree; there are no standards in the field," Ghani explains. "You could use Excel and call it data science, or you could be doing complicated optimization and deep learning."

Learn more: Is an MSDS worth it?

According to Ghani, the perfect master’s degree is easy to describe but hard to find. That’s because programs face an additional challenge: cramming essential skills into two years. Unlike software engineering roles with clear specifications, data scientists must define problems, locate and clean data, build solutions, and navigate ethical dilemmas—often with no roadmap.

For those uncertain about choosing between a master’s in data science and a master’s in AI, he says that the two fields are quite comparable. “From the education perspective, the programs are very similar. The students are very similar. The content is very similar. They all talk about ‘how do we use data to improve some sort of decision-making.’”

Ghani predicts that future programs will shift toward breaking disciplinary silos. "You’ve got data science, statistics, machine learning, econometrics, operations research, behavioral economics—no single problem is going to be solved by just one of these boxes," he says.

Research programs—and find your niche

When selecting programs, Ramasamy says students should pay close attention to the curriculum, including how in-depth it goes into various levels of analytics and modeling. Importantly, he adds, students should inquire about the available real-world tools.

“I would look for programs which are partnered in industry,” he advises. Databricks, for example, has a university alliance with Duke University and UC–Berkeley along with three other international schools to give classrooms access to industry resources.

His biggest piece of advice for students: being a generalist is fine in the beginning, but once you find something that strikes your interest, focus on that area—whether it be sports, healthcare, or biology. 

“Depending upon their interest, they should apply to the appropriate vertical, because you don't want to go into a vertical that you're not interested in,” he says.

You need to be well equipped to have many hats, including someone who is comfortable with analyzing, processing, cleaning, and visualizing data—and with that comes knowing how to use the appropriate tools, software, and more.

“Knowing the tools alone is not enough; you should know more about the underlying systems, how it works and all, so that you can understand why the analytics is coming like this,” he says.

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