Methodology

More institutions than ever have established master’s in data science programs. Fortune attempted to contact over 90 institutions to participate in this year’s ranking. Thirty-five universities filled out our data questionnaire, which asked for information on schools’ admissions policies, admitted students, and graduates.

• Previous ranking/participation: 5%
• Annual search volume: 5%
• Total program cost, out-of-state U.S. residents: 15%
• Acceptance rate, academic year 2023-2024: 10%
• Yield, 2023–2024: 10%
• Average undergraduate GPA, 2023–24 enrollees: 10%
• Graduation rate, 2021-24: 20%
• One-year retention rate, 2023–24: 25%

Learn more: Read Fortune’s master’s in data science methodology.

Data science career paths 

What is a master’s in data science?

A master’s in data science is a graduate degree at the intersection of mathematics and statistics with computer science. Students will learn to use computer programming languages while studying applied statistics, database systems, and machine learning. The degree prepares students for a wide variety of tech roles, no matter the format of the program; schools tend to offer identical courses online and in-person—taught by the same professors with the same curriculum.

Data science curriculum and skills taught

Data science master’s programs have a comprehensive curriculum that includes both statistical and computational methods. Programs will emphasize the real-world application of these knowledge and skills while offering a multidisciplinary approach to the field that also draws on statistics, computer science, and law. 
Data science is about more than numbers; students will also learn soft skills like problem-solving, critical thinking, and communication. For data science in particular, the latter is important as students collaborate on the ethics of data. 

Core coursework at many data science programs covers the following topics:

• Cloud computing
• Data engineering
• Data mining
• Data visualization
• Information ethics
• Machine learning
• Research design
• Statistical analysis

Students may learn how to properly use software such as Alteryx, Databricks, and Power BI alongside computer languages like Python, R, and SQL.

Data science project-based learning

Project-based learning is key to any top data science program, as it is in real-world scenarios when students best learn the necessary skills to succeed. This is often embodied in the end of the program, where students may be required to enhance in a capstone or thesis that is tailored to students’ interests. Over half of the programs on Fortune’s ranking require students to enhance in either a capstone or thesis project at the end of their studies.

Data science specializations and concentrations

While the core coursework required for completing a master’s degree in data science is intentionally comprehensive, many programs offer specializations or concentrations so students can carve out a niche within this field. For instance, Michigan State University allows students to focus on subjects like MLOps, Deep Learning, or Optimization. Depaul University offers concentrations in computational methods, healthcare, and marketing. The University of Missouri has tracks in biohealth analytics, geospatial analytics, high-performance computing, communication & data journalism, and human-centered science design.

Admissions information for master’s in data science

While specific requirements may differ between institutions, most graduate programs in data science typically expect the following from prospective students:

• Academic Background: A bachelor's degree is mandatory, with applicants required to submit official transcripts from an accredited college or university as proof of completion.
• Relevant Experience: For those without an undergraduate degree in a data-related field, demonstrating sufficient work or educational experience in key concepts through their resume may be necessary.
• Personal Statement: Applicants often have the opportunity to showcase their unique qualities and program objectives through a personal statement or essay.
• Recommendations: Letters of recommendation from mentors, professors, or program alumni are typically required to support the application.
• Standardized Testing: Although many prestigious data science programs don't mandate GMAT or GRE scores, these may be required if requested.
• Additional Requirements: Some master's programs in data science might include a data proficiency examination as part of the admissions process.

GMAT, GRE, and GPA requirements for master’s in data science

Many online master's programs in data science have eliminated the need for GRE or GMAT scores. However, applicants can still choose to submit these test results if they believe it will strengthen their application. It's worth noting that GPA requirements differ among institutions and may be waived for candidates with substantial professional experience.
While the absence of mandatory test scores might seem to simplify the admissions process, it doesn't necessarily make acceptance easier. Instead, it shifts the focus to other components of the application, which become more crucial in the evaluation process. This change emphasizes the importance of presenting a well-rounded and compelling application package. The average acceptance rate of the schools on Fortune’s ranking is about 42%.

Which factors drive acceptance into a master's in data science program?

While admissions officers strive to take a holistic approach when evaluating candidates, they will be particularly interested in your educational background and work experience in a data-related field. Some data science programs require applicants to have completed specific quantitative college-level coursework. Other programs prioritize the personal essay, focusing on the applicant’s reasons for choosing the program and their goals.

How to choose the best master’s degree program in data science for you: Factors to consider beyond rankings 

While rankings are a great way to explore and compare programs, prospective students should consider how a particular program will help them achieve their goals and advance in the field of data science. Other factors that may be important include total cost, a school’s prestige, its curriculum, and the years of work experience schools may require of applicants.

Start times, schedule, and program length

With the growing popularity of data science programs, many schools now offer multiple start dates throughout the year. This increased flexibility allows students to begin their studies at a time that best suits their personal and professional commitments. While two years is a typical duration for completing these programs, some institutions provide options for accelerated or extended timelines.

Project-based learning

Many data science programs incorporate project-based learning experiences, focusing on real-world applications of the skills taught. These projects can serve as valuable additions to a student's portfolio, potentially making them more attractive to future employers. For career changers, prioritizing programs with such hands-on opportunities might be particularly beneficial, even if it slightly extends the overall program length.

Concentrations

When considering your post-graduation career goals, it's important to explore the various concentrations offered within data science programs. Specializing in a particular area of data science can enhance your appeal to certain employers and potentially increase your earning potential. For instance, professionals with the title "data scientist" can earn up to $170,000, while those in managerial roles in the field may command salaries as high as $250,000.

Cost

The cost of a data science program is undoubtedly a factor to consider when applying to school—and tuition varies widely. Students can expect to pay less than $40,000 in total at many programs, including:

• University of Delaware
• University of North Texas
• Clarkson University
• Mercyhurst University
• University of St. Thomas
• Texas Tech University (Rawls)
• University of Connecticut
• Michigan State University

On the other side of the spectrum, total tuition is over $80,000 at several schools, including:

• New York University
• University of Virginia
• University of Michigan–Ann Arbor
• Harvard University (Paulson)
• Johns Hopkins University (Whiting)

Learn more: Check out our list of the best master’s in data science by affordability. 

Network and access to alumni

The size of a data science program's student body and alumni network can significantly impact your graduate school experience and future career prospects. Even in online programs, your cohort can play a crucial role in shaping your educational journey. Moreover, a robust alumni network can provide valuable connections and job opportunities, particularly beneficial for those transitioning into the field. When selecting a program, consider the strength of its alumni network and the school's ability to facilitate connections with industry professionals.

Immersive experiences (on-campus visits)

If you’re hesitant about attending classes online, some MPH schools offer immersive experiences that incorporate on-campus elements in the program. This may be a factor to consider if you want the opportunity to network in person or be close to campus to take advantage of resources like the library. What’s more, many programs include hands-on practicums or capstones such as an internship.

Years of work experience

Many data science students come to the program with at least a few years of work experience, but because the field is expanding so quickly, many students are relatively young. American University, Clarkson University, Syracuse University, and Worcester Polytechnic Institute report that their master’s in data science have an average of three years of work experience.

Careers for master’s in data science graduates

The job market for data scientists is thriving, with many graduates receiving multiple job offers with six-figure salaries. Major tech companies like Microsoft, IBM, and Meta are significant employers in this field, often offering competitive compensation packages for top talent. However, opportunities extend beyond tech giants, with data science skills in high demand across various industries.

Financing and scholarships

Numerous private organizations provide scholarships and fellowships for data science students. Some notable examples include:

• The Association of Computing Machinery (ACM) offers computational and data science fellowships with a $15,000 annual stipend for candidates from underrepresented groups.
• Acxiom provides $5,000 scholarships to U.S.-based students from diverse backgrounds enrolled full-time in data science-related programs.
• The American Statistical Association (ASA) offers a Pride scholarship for LGBTQ+ students or allies enrolled in data science graduate programs.

Current service members or veterans may be eligible to cover their data science program costs using Post-9/11 GI Bill benefits. The Yellow Ribbon Program can potentially cover any remaining tuition and fees not addressed by the GI Bill. By exploring these various funding options, you can make your data science master's degree more financially accessible and focus on your studies and career development.

Frequently asked questions

What is data science?

Data science is a multidisciplinary field that involves preparing and analyzing data to derive insights. It encompasses designing and building processes for data modeling using algorithms, prototypes, predictive models, and custom analysis. This field is ideal for those interested in asking questions and creating statistical models to estimate unknowns.

Is data science in high demand?

The demand for data scientists is exceptionally high. Global data is projected to reach 181 zettabytes by 2025, driving this demand. According to the U.S. Bureau of Labor Statistics (BLS), data science is the third-fastest growing occupation in the country.

How can I become a data scientist?

A common path to becoming a data scientist includes: 

1. Pursuing an undergraduate degree focusing on technical skills like programming or statistics
2. Identifying and specializing in a specific area of data science
3. Enrolling in a master's degree program in data science
4. Showcasing your experience when applying for jobs

Is a master’s degree in data science worth it?

A master's in data science can be highly valuable. The BLS projects 36% job growth for data scientists through 2033. Many students receive job offers with salaries of $108,020 or more even before graduation.

How much money can you make in data science?

On average, data scientists earn over $100,000 annually. However, salaries can vary significantly based on company, role, and experience.

What is the highest-paying data science job?

According to Glassdoor, the median total pay for data scientists is about $164,000, but that can grow to over $200k or $300k for senior-level positions within tech companies.

What kind of jobs do data scientists do?

Data scientists have diverse career opportunities across industries including tech, entertainment, pharmaceuticals, telecom, sports, and consulting. New roles are continually emerging, particularly in areas related to ethical data handling and emerging technologies.

How hard is it to get into a data science masters?

In-person master's in data science programs can be tough to get into, with acceptance rates near 40%. For online programs, the averages are higher—at around 70%.

Can I be a data scientist without a master's degree?

While a master's degree is not strictly required, it can significantly enhance career prospects, especially for those looking to advance in tech careers or transition into data science.

What GPA do you need for a master’s in data science?

While many online programs don't have strict GPA requirements, the average undergraduate GPA of admitted students is typically around 3.4.