An online master’s in data science is a graduate degree for two very different students: the career changer who holds a quantitative degree in another field and needs the modeling and computing core, and the working analyst who wants to move from reporting into modeling. Titles vary – MS in Data Science, MS in Applied Data Science, MS in Analytics, MS in Statistics with a data science track, and occasionally an MS in Computer Science with a machine learning concentration. The required course list and the prerequisite list tell you far more than the title does.
This page explains how these programs are built, who they are actually designed for, what the prerequisites screen out, and what to compare across schools.
Quick answers
What is an online master’s in data science?
It is a graduate program covering statistical inference, machine learning, programming, data management at scale, and a capstone or thesis, delivered online. Most programs run 30 to 36 semester credits, though totals vary, and many add bridge courses for applicants without a calculus and programming background.
Do I need a computer science or statistics bachelor’s to be admitted?
Not usually, but you need the prerequisites. Most programs expect calculus, linear algebra or an equivalent, an introductory statistics course, and demonstrated programming in Python or R. Programs aimed at career changers often let you satisfy these with bridge courses before the core begins. Read the prerequisite list before the curriculum; it is the real admissions gate.
Is a master’s required to be a data scientist?
No. The Bureau of Labor Statistics lists a bachelor’s degree as the typical entry-level education for Data Scientists. A large share of postings still ask for a master’s, especially for research-facing and modeling-heavy roles, and the degree is a common route for people entering from an unrelated field. It is not a license, and no exam gates the title.
Thesis or capstone?
Most applied programs use a capstone: a team or individual project on real data, often with an industry partner. Thesis tracks exist and are worth choosing if you might pursue a doctorate or want research experience. A capstone you can publish and discuss in interviews is usually more valuable for industry hiring than a thesis you cannot show.
How long does it take?
Calendars vary by program, credit total, and whether you enroll full-time or part-time alongside work. Confirm total credits, the number of bridge courses you will need, and how often each required course is offered online, rather than assuming a single national timeline.
What does a master’s change about pay?
Data Scientists earned a median annual wage of $120,230, with the 10th percentile at $67,240 and the 90th at $199,130 (Bureau of Labor Statistics, May 2025 OEWS). BLS does not publish wages by degree level within the occupation, so treat any specific salary bump attributed to the degree with suspicion. What the degree reliably changes is which postings will consider your application.
At a Glance
- Degree types: MS in Data Science, MS in Applied Data Science, MS in Analytics, or a statistics or computer science master’s with a data science track
- Typical size: Around 30 to 36 semester credits, plus any bridge coursework
- Admissions: Bachelor’s in any field plus calculus, linear algebra, statistics, and programming prerequisites
- Capstone: Applied project on real data, or a thesis in research-oriented tracks
- Accreditation: No widely used programmatic accreditor at the master’s level; verify institutional accreditation
Schools to compare
How We Rank Schools
Every school list on this site is ordered by the BOC Score, computed from the most
recent school-level data published by the U.S. Department of Education (College Scorecard and IPEDS).
To qualify, a school must be currently operating and accredited by an agency recognized by the
U.S. Department of Education. Each eligible school is then scored on five measures, percentile-ranked
against schools at the same credential level:
BOCSCORE
- Graduation rate 30%
- Median earnings, 10 years after entry 25%
- Average net price (lower is better) 20%
- Retention rate 15%
- Fully online availability 10%
Schools without enough outcome data appear after ranked schools, without a score.
Advertising never affects these rankings. Read the full methodology.
Graduation rate
93%
Median earnings, 10 yrs after entry
$102,772
Avg net price
$12,116/yr
TuitionContact school for pricing
Key stats
- Retention rate: 98%
- Programs offered: 64
Source:IPEDS · College Scorecard
Graduation rate
97%
Median earnings, 10 yrs after entry
$124,080
Avg net price
$13,807/yr
TuitionContact school for pricing
Key stats
- Retention rate: 98%
- Programs offered: 100
Source:IPEDS · College Scorecard
Acceptance rate
8%
Graduation rate
95%
Median earnings, 10 yrs after entry
$89,718
Avg net price
$13,370/yr
TuitionIn‑state$64,144
Out‑of‑state$64,144
Key stats
- Retention rate: 98%
- Programs offered: 78
Source:Accreditor: Southern Association of Colleges and Schools Commission on Colleges · IPEDS · College Scorecard
TuitionContact school for pricing
Key stats
- Retention rate: 96%
- Programs offered: 30
Source:IPEDS · College Scorecard
Acceptance rate
33%
Graduation rate
84%
Median earnings, 10 yrs after entry
$74,915
Avg net price
$16,109/yr
TuitionIn‑state$16,414
Out‑of‑state$50,614
Key stats
- Retention rate: 92%
- Programs offered: 81
Source:Accreditor: Western Association of Schools and Colleges Senior Colleges and University Commission · IPEDS · College Scorecard
Graduation rate
84%
Median earnings, 10 yrs after entry
$66,240
Avg net price
$20,781/yr
TuitionContact school for pricing
Key stats
- Retention rate: 88%
- Programs offered: 29
Source:IPEDS · College Scorecard
Graduation rate
91%
Median earnings, 10 yrs after entry
$64,027
Avg net price
$27,579/yr
TuitionContact school for pricing
Key stats
- Retention rate: 93%
- Programs offered: 54
Source:IPEDS · College Scorecard
Graduation rate
83%
Median earnings, 10 yrs after entry
$67,258
Avg net price
$28,651/yr
TuitionContact school for pricing
Key stats
- Retention rate: 88%
- Programs offered: 40
Source:IPEDS · College Scorecard
Typical topics in a master’s program
| Course Topic |
What You Learn |
| Statistical inference |
Estimation, hypothesis testing, likelihood, and the assumptions behind the tests you will run |
| Machine learning |
Supervised and unsupervised methods, model selection, regularization, and honest evaluation |
| Deep learning or advanced modeling |
Neural network architectures, training dynamics, and when the simpler model wins |
| Data engineering and databases |
Pipelines, warehousing, distributed processing, and moving data you cannot fit on a laptop |
| Experimental design and causal inference |
A/B tests, quasi-experiments, confounding, and what observational data cannot tell you |
| Data visualization and storytelling |
Presenting a model’s result and its uncertainty to a decision maker |
| Ethics, privacy, and governance |
Bias auditing, de-identification, and constraints on regulated data |
| Domain elective |
Health, finance, marketing, geospatial, or text and language applications |
| Capstone or thesis |
An end-to-end project, defended, and ideally publishable in a portfolio |
Who the degree actually fits
Career changers with a quantitative background get the most out of it. If you hold a degree in economics, physics, biology, engineering, or mathematics, a master’s supplies the computing and modeling layer you are missing and, more importantly, supplies projects and a credential that make your application legible to a recruiter who does not know your field.
Working analysts moving toward modeling are the second group. If your job is already SQL and reporting, the degree adds machine learning, experimental design, and engineering practice. Some of that is learnable on the job; the parts that usually are not are causal inference and rigorous evaluation.
Students with no quantitative background at all should be careful. A program that admits you without calculus, linear algebra, or programming and offers no bridge sequence is not being generous. It is likely to be teaching tools rather than methods, and the graduate will compete against people who can derive what they are running. Ask what the prerequisites are; a program with real prerequisites is telling you something about its core.
Recent computer science or statistics graduates often get the least marginal value. You already hold the core, and a first job plus a portfolio may move you further than another degree. See is an online data science degree worth it.
MS in Data Science vs neighboring master’s degrees
An MS in statistics goes deeper on inference, probability theory, and experimental design, and lighter on engineering. An MS in machine learning or artificial intelligence concentrates on model architecture and research methods rather than on the full analysis pipeline. An MS in computer science trains you to build software; the data work becomes a specialization inside it. An analytics master’s leans business-facing and lighter on math; if that is the target, compare the data analytics guide first. The AI degree vs data science degree comparison covers that boundary in detail.
What to compare across programs
- Prerequisite list, and whether bridge courses are included in the credit total or billed on top
- Whether machine learning is taught with the mathematics or as a library tour
- Whether experimental design and causal inference appear at all, since many programs skip them
- Total required credits and how often each required course runs online
- Capstone structure: individual or team, real data or a teaching dataset, and whether you may publish it
- Compute environment provided, including cloud credits and GPU access for deep learning coursework
- Whether faculty are practicing in the field, and who supervises capstones
For pacing and delivery comparisons, see How Online Data Science Degrees Work.
How to compare online master’s programs
- Read the prerequisite list first, and be honest about which ones you hold.
- Confirm whether bridge coursework adds credits, terms, and cost.
- Check that the machine learning sequence has a probability prerequisite.
- Look for experimental design or causal inference in the required core, not only as an elective.
- Ask what the capstone uses for data and whether the work is yours to show.
- Confirm the compute environment rather than assuming the school supplies GPUs.
- Verify recognized institutional accreditation through the U.S. Department of Education database.
No widely recognized programmatic accreditor evaluates master’s-level data science programs. Institutional accreditation is the check that matters. A degree does not guarantee a title, a salary, or an interview – the projects you can show do a large share of that work.
Admissions requirements
Requirements vary, but most programs ask for a completed bachelor’s degree in any field, transcripts, a statement of purpose, a resume, and letters of recommendation. Standardized test scores are frequently optional. The distinctive item is the quantitative prerequisite check: calculus, linear algebra or an equivalent, introductory statistics, and evidence that you can program.
Applicants often ask whether work experience substitutes for the prerequisites. Sometimes, when the work was genuinely technical, but admissions committees want to see the coursework or a demonstrable body of code. If you are short, community college calculus and linear algebra taken before you apply is the cheapest fix, and it also tells you whether you enjoy the material before you commit to the degree.
Master’s vs a bachelor’s pathway
An online bachelor’s in data science is the undergraduate credential plus the quantitative core, and it is the typical entry-level education BLS lists for the occupation. A master’s is a second credential that adds depth and, for career changers, converts an unrelated background into a legible one. It is not required to enter the field, and it does not replace a portfolio.
Compare degree options:
For the value discussion, see Is an Online Data Science Degree Worth It. If your target work is reporting and business metrics rather than modeling, see the Data Analytics Program Guide instead.
Data verified: September 5, 2026.
Salary, employment, and tuition figures on this page are sourced from the U.S. Bureau of Labor Statistics (OEWS May 2025; Employment Projections 2024–2034) and the U.S. Department of Education College Scorecard (2023 cohort). The source agency and data year are cited inline with every statistic.
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