What Does it Take to Earn a Master's in Data Science?

Data science is rapidly becoming one of the most important and fastest-growing fields worldwide. From healthcare and finance to technology and retail, companies and organizations at every level rely on data scientists to solve problems and make smarter decisions.

The 2025 State of Data & AI Literacy report shows that 86% of business leaders think data skills are essential for everyday work, and the U.S. Bureau of Labor Statistics predicts data science jobs will grow 36% between 2023 and 2033, much faster than the national average.¹^,²

This growth means that, whether you’re a recent college graduate, mid-career professional, or looking to transition into the field, completing a graduate program in data science can open the door to high-potential roles, competitive salaries, and long-term career stability. Depending on your goals, you can choose from various graduate pathways, including full-time master’s degrees, professional certifications, or flexible online master’s programs.

If you are thinking of applying to a master’s in data science program, it’s important that you do your research to learn everything you can about the admission requirements and the application process for the programs that interest you.

Educational Background

Getting admitted into a master’s in data science program requires a bachelor’s degree, but not necessarily in data science or STEM. While a background in computer science, mathematics, or statistics may make the transition to graduate study easier, what matters most is your grasp of the following foundational concepts:

But even if you are considering an MS in Data Science from a non-STEM background and feel underprepared, you can choose universities that offer preparatory coursework to help you build the necessary foundation.

One example of this is the University of New Haven, where students without sufficient Python skills are required to take the Introduction to Programming for Data Science bridge course. You can also learn and practice foundational skills through free data science courses on sites like Coursera, edX, and Udemy.

Transcripts & GPA

As part of your application, you’ll need to submit official transcripts from all prior institutions. Most data science degree programs require a minimum GPA of 3.0, but this varies. GPA is considered alongside other factors like experience, test scores, and personal statements. If you are still completing your bachelor’s program but know you plan to apply to master’s programs, you have a few ways to strengthen your application:

GRE/GMAT Requirements

The role of standardized tests like the Graduate Record Examination (GRE) and Graduate Management Admission Test (GMAT) is changing. Many programs no longer require them, especially online or part-time tracks. Examples:

GRE and GMAT waivers are more common for mid-career professionals, whose work history, letters of recommendation, and statement of purpose carry more weight than test scores.  

Letters of Recommendation

Most master’s degree programs require at least two letters of recommendation. Choose recommenders who can speak to your readiness for graduate-level work.

Strong letters highlight intellectual ability, professionalism, and leadership potential.

Personal Statement/Statement of Purpose

Your statement of purpose should explain:

These elements give admissions teams a clear picture of your academic readiness and fit for a data science program.

In addition to academic credentials, most programs expect applicants to demonstrate some level of technical fluency, particularly in programming, mathematics, and data analysis. Admissions committees look for candidates who can apply data science techniques to solve real-world problems, whether through coursework, self-study, or hands-on experience.

According to a 2023 report by the Burning Glass Institute, about 22% of U.S. job postings across all industries mention at least one data science-related skill, highlighting the widespread demand for technical fluency.³

While you don’t need to be an expert coder to apply, basic programming experience is important. Many programs also welcome applicants from non-traditional backgrounds, such as bootcamp graduates or social science majors, if they can show strong analytical thinking and comfort with technical tools.

Key skills and knowledge areas include:

Some universities offer bridging modules or prep courses to help you build these skills before fully entering the program.

Pursuing a data science degree is more than just a stepping stone but an act of mastering the must-have skills behind every data-driven breakthrough. Whether your goal is to become a data scientist, data engineer, or business leader fluent in big data analytics, here are the top skills you’ll want to build:

Applied Machine Learning & Artificial Intelligence

A core part of any data science degree is mastering applied machine learning and artificial intelligence techniques. You'll build predictive models and intelligent systems using:

Big Data Tools & Technologies

To thrive in roles like data engineer or big data analyst, you’ll need hands-on experience with:

Data Mining & Data Analytics

Understanding how to uncover and interpret data patterns is important:

Data Visualization & Communication

Turning data into insight means telling a clear, visual story. You’ll use:

While technical skills are critical, many applicants wonder whether professional experience is also necessary. The answer depends on the program and your experience. Many schools accept applicants with little or no professional background, as long as they show strong technical skills in key topics like programming, statistics, and applied machine learning.

However, some programs, particularly part-time or executive formats, prefer mid-career professionals. These applicants bring real-world context to classroom discussions and are often looking to apply data science tools directly to their current roles.

If you are still early in your career, a portfolio can be a powerful way for you to showcase readiness for graduate study. Admissions committees often value GitHub repositories, personal projects, or contributions to data science competitions as proof of technical ability and motivation. Some programs may also request coding supplements or task-based assessments as part of the application.

Some online master’s in data science programs may be more accessible than traditional on-campus options, as many don’t require the GRE, waive application fees, and welcome people from various academic or work backgrounds. This makes them an excellent choice for career changers or anyone just starting in data science.

While on-campus programs tend to follow a more traditional admissions process, they may expect a stronger academic foundation in computer science, statistics, or mathematics, and often prioritize applicants with research experience or higher GPAs. These programs are usually structured, full-time commitments with a stronger emphasis on in-person engagement, research labs, and campus recruiting opportunities.

For professionals already working in the field, online MS in Data Science programs offer the flexibility to keep their jobs while studying. The ability to apply new skills immediately is a major benefit. On the other hand, on-campus degrees may offer more accessible networking, faculty, and campus experiences.

International students must demonstrate English proficiency by submitting official scores from an approved exam. Most programs accept the Test of English as a Foreign Language (TOEFL) or the International English Language Testing System (IELTS). For the TOEFL, a minimum score of 550 on the Paper-Based Test (PBT) or 80 on the Internet-Based Test (IBT) is typically required. 

For IELTS, a minimum overall band score of 6.5 is standard. Applicants from English-speaking countries may be exempt. These scores must be submitted as part of the application process. In addition to test scores, international students must provide a copy of a valid passport and academic records, including diplomas or degrees earned. If your documents are in another language, be sure to include certified English translations..

Applicants must also submit financial documentation, either a bank statement from the past six months or a sponsorship letter explaining how the program will be funded. Most programs expect a minimum undergraduate Grade Point Average (GPA) of 3.0 on a 4.0 scale or the international equivalent.

Most master’s in data science programs follow fixed admissions cycles, with deadlines typically set between December and March for fall entry. Some schools offer multiple start dates or rolling admissions, depending on the program format. 

For example, the University of Notre Dame’s online data science program accepts applications for both summer and fall intakes, with deadlines for February 2 and May 4, 2025. The University of Washington has set deadlines for its autumn intake: January 7, 2025, for international students and January 14, 2025, for domestic applicants.

It’s a good idea to begin the application process at least three months ahead of the deadline to produce polished, complete materials that reflect your full potential. Here are some tips for building a strong application.

A master’s in data science is more accessible than it might seem, especially if you prepare strategically. Whether from a STEM background or changing careers, the key is demonstrating motivation, foundational skills, and readiness to grow. With the right mix of planning, learning, and persistence, many paths can lead you into the field. Explore available programs, review their requirements, and take the next step toward your data science career with confidence.