How to Choose the Best MS in Data Science Program for You

Start by defining three constraints before you browse any program pages: how much time you can commit, whether you need online or on-campus delivery, and whether you can take the GRE. Then compare programs on the same five dimensions — curriculum coverage, faculty and delivery format, time to completion, admission requirements, and how the program connects you to employers. MastersinDataScience.org organizes its guide around exactly these decision points, and the two sponsored programs it features illustrate how much programs can differ on each one.

What the site covers, and why that shapes your comparison

MastersinDataScience.org is a guide to data science graduate programs and upskilling options, owned by 2U, LLC (the parent company of edX). It states that some listed programs come from universities partnering with 2U, which provides marketing and operational support and receives compensation, and that other listings may be paid advertisements. Both types are labeled "AD" or "Advertisement."

That disclosure matters for how you use the site: treat it as a starting point for building a comparison list, not as a neutral ranking. Cross-check any program you shortlist against the university's own department page before applying.

The site's guide sections map directly onto the questions you need to answer:

• What is data science? (scope of the field)
• Master's in data science programs and related degrees (what types exist)
• Choosing the best MS in data science program for you (evaluation criteria)
• Sponsored online master's programs (specific listings)

The five dimensions to compare

1. Curriculum coverage across the data life cycle

Look for whether the program covers the full pipeline or only part of it. The site describes data science work as spanning planning and gathering data, analyzing it, and presenting findings.

UC Berkeley's Master of Information and Data Science (MIDS), as described on the site, takes what it calls a multidisciplinary approach to the data life cycle and includes coursework on privacy, machine learning, and data storage and management. UNC-Chapel Hill's Master of Applied Data Science is described as covering the data life cycle plus advanced programming and machine learning.

If your goal is a specific role — say, ML engineering versus analytics leadership — check which end of that pipeline the required courses actually sit in.

2. Faculty and delivery format

Delivery format affects whether you can keep working and how much structure you get. Berkeley's MIDS is described as delivering class sessions live online, taught by many of the same faculty and industry professionals who teach on campus. That's a meaningful difference from fully asynchronous programs, where you lose scheduled interaction but gain flexibility.

Ask each program: Are sessions live or recorded? Who teaches — full-time faculty, adjuncts, or industry practitioners? What's the typical class size?

3. Time to completion

The two featured programs differ here, and both state their timelines explicitly:

Note the qualifier "full time" on the UNC figure — part-time timelines are typically longer. If you're working while studying, ask for the part-time completion window separately, since that's the number that will actually apply to you.

4. Admission requirements, including the GRE

Both featured programs state that no GRE is required. This is not universal across data science master's programs, so make it an explicit filter: if you don't want to take the GRE, confirm the requirement in writing for every program on your list rather than assuming.

Other admission factors to verify per program: prerequisite coursework in math, statistics, or programming; minimum GPA; and whether work experience substitutes for any of these.

5. Career connection

UNC's program is described as offering opportunities to connect with industry professionals in North Carolina and beyond. Berkeley's is positioned for professionals aiming at leadership roles in the field.

Career support varies widely — some programs run employer networking events, others rely on alumni networks. Ask what specifically is provided: career fairs, capstone projects with company sponsors, alumni mentoring, or job placement data.

A practical comparison workflow

1. Write down your constraints first. Available hours per week, target start date, GRE willingness, and budget range.
2. Build a shortlist of 5–8 programs using the site's program listings and related-degree sections.
3. Fill in the same table for each — curriculum focus, delivery, time to completion, GRE requirement, career support. Use identical criteria so the comparison is fair.
4. Verify every data point on the university's own site. Listings on aggregator sites, including sponsored ones, can lag behind current requirements.
5. Talk to someone in the program — an admissions contact or a current student — about the two or three factors you can't evaluate from a webpage, such as workload realism and how responsive faculty are.

Common sticking points

• Confusing "as few as" timelines with typical timelines. A 12-month completion usually assumes full-time enrollment and may require prior coursework.
• Assuming no-GRE applies everywhere. It's program-specific, not field-wide.
• Treating sponsored listings as rankings. The site labels advertisements; a listing's position doesn't indicate quality.
• Skipping the part-time question. If you'll study while employed, the full-time figure is irrelevant to your planning.

Where to go next

The site's guide covers what data science is, the range of master's and related degrees available, and how to evaluate them, plus a section on taking the next step toward earning a master's. Use those sections to build your shortlist, then do the verification work on each university's own pages before you apply.