Data science sits at the intersection of statistics, programming, and domain expertise, which makes it an unusually good fit for online graduate education — most applicants are already working professionals with some quantitative background, looking to formalize and extend skills they're already using on the job rather than starting from zero. A handful of established universities now run online Master's programs in this space that carry real weight. Here's how the leading ones compare.
The Leading Programs
| Program | University | Notable For |
|---|---|---|
| OMS Analytics (Online Master of Science in Analytics) | Georgia Institute of Technology | Extremely low cost, built on the same scaled-online model as OMSCS |
| MIDS (Master of Information and Data Science) | UC Berkeley | Strong brand recognition; broader information-science framing alongside core data science |
| MADS (Master of Applied Data Science) | University of Michigan | Delivered via Coursera; strong applied/practitioner focus, automatic scholarship consideration |
Does the Credential Match the On-Campus Degree?
This varies more across data science programs than it does for the flagship online CS Master's programs, so it's worth checking individually rather than assuming. Georgia Tech's OMS Analytics is an interdisciplinary degree developed specifically for online delivery at scale, run jointly across the College of Computing, Scheller College of Business, and the School of Industrial and Systems Engineering — it's a legitimate, fully accredited Georgia Tech Master's degree, though it was built as an online-first program rather than a direct mirror of a single on-campus department's degree. Berkeley's MIDS is likewise a purpose-built online program from the School of Information, delivered by the same faculty who teach Berkeley's on-campus programs, with the same admissions rigor and coursework standard. Michigan's MADS is similarly a proper Michigan School of Information degree, delivered online through Coursera with faculty from the same department.
In all three cases, the degree is a genuine, regionally accredited Master's from the university in question, taught largely by the same faculty as the university's other graduate programs — the key thing to verify with any specific program is whether it awards the exact same named degree as an existing on-campus program, or a purpose-built online degree from the same school and department. Either can be a legitimate, valuable credential; the distinction mostly matters for how you describe and think about the degree afterward, not for its accreditation or rigor. See our accreditation guide for how to verify legitimacy of any specific program you're considering.
Cost and Format
| Program | Approximate Total Cost | Format Notes |
|---|---|---|
| Georgia Tech OMS Analytics | Among the lowest-cost options in this category, broadly comparable to OMSCS pricing | Asynchronous, part-time friendly, large scaled cohorts |
| UC Berkeley MIDS | Significantly higher — commonly well into six figures in total tuition and fees | Mix of asynchronous coursework and live synchronous sessions; smaller cohort feel than Georgia Tech's programs |
| University of Michigan MADS | Mid-range; in-state and out-of-state rates differ, with per-credit pricing | Asynchronous, Coursera-hosted, automatic scholarship consideration for applicants |
Confirm current tuition directly on each program's site before applying — these figures shift from year to year and, for Michigan and Berkeley in particular, vary further by residency and current fee schedules.
The format pattern across all three programs follows the same general shape as online CS Master's programs: mostly asynchronous video lectures and coursework, built around the assumption that most students are working full-time, with course loads typically spread over 18 months to 3 years depending on pace. Berkeley's MIDS leans somewhat more heavily on live synchronous class sessions than Georgia Tech's OMS Analytics or Michigan's MADS, which is worth factoring in if your schedule has limited flexibility for fixed weekly commitments.
Who These Programs Are Designed For
These programs are built around a fairly consistent target applicant: a working professional with some existing quantitative background — a undergraduate degree in a STEM field, statistics, economics, engineering, or a role that already involves data analysis — who wants to formalize that experience into a credentialed, graduate-level data science skill set without leaving their job. Admissions typically looks for evidence of quantitative coursework (statistics, linear algebra, some programming) even if the applicant's degree wasn't explicitly in a "data" field.
They're a strong fit if you're already working in a data-adjacent role and want to move into a more specialized data science or machine learning position, or if you're a working professional in a quantitative field looking to formally credential skills you've picked up on the job. They're a weaker fit if you're starting from little or no quantitative or programming background, or if you specifically need the in-person research access, on-campus recruiting pipeline, or full-time cohort experience that an on-campus program provides — see our broader look at whether an online degree is worth it for a fuller framework on making that call.