2027 Michigan MADS vs. Notre Dame Online MS in Data Science: Cost, Curriculum, Admissions, and Format

The clearest established distinction is the credential: Michigan offers a Master of Applied Data Science, while Notre Dame offers a Master of Science in Data Science. "Applied" may signal the kind of work a prospective student wants to do, but course requirements and projects - not naming - are the sound basis for comparison.

The table separates identifiable differences from consequential details that need confirmation for your intended entry term.

A useful comparison starts with the work you want to produce. A student aiming for production-oriented analytics should examine programming, data engineering, and deployable projects; one aiming for research-heavy modeling should examine statistics, machine learning, and opportunities for advanced study. Check the actual required courses at both schools before treating either emphasis as established.

Neither program is automatically the better online option. The deciding question is how its current courses operate: whether instruction can be completed asynchronously, whether live meetings are required, and how much control students have over course load. An online label does not resolve those scheduling details.

If you work variable hours, prioritize a written account of attendance, exam windows, group-meeting expectations, and the policy for pausing enrollment. If you prefer scheduled collaboration, ask how often classmates and instructors meet and whether attendance is mandatory. Get answers for required courses, not just a sample elective; an appealing format is useful only if it works through graduation.

A defensible current price comparison requires each university's tuition schedule and degree requirements for the same entry term. Without verified figures for both, stating a total or naming a cheaper program would risk comparing outdated rates or unlike cost categories. Ask each school for a written estimate covering tuition for the complete degree, mandatory university and program fees, and any required travel, software, or materials.

Then separate that billed estimate from financing costs and earnings you might give up if you reduce work hours. Ask whether an employer will reimburse either program and under what conditions. Data science master's financial aid options can help frame that conversation, but eligibility and award amounts must be confirmed with the schools.

A common mistake is to compare one school's per-credit tuition with the other's estimated degree total. Obtain the credits you must complete, the rate that applies to you, expected fee changes, and the terms of any aid before calculating your out-of-pocket difference.

Use the latest required-course lists and descriptions to compare both programs on a common basis: statistical reasoning, programming, data management, machine learning, communication, and responsible use of data. Count which subjects are required, which are elective, and which culminate in assessed work. A topic mentioned in a course description is not equivalent to a dedicated course or substantial project.

For Michigan MADS, test whether the required applied work matches the systems and decisions you want to handle on the job. For Notre Dame, test the same question against its MS requirements rather than assuming that the broader degree title means more theory. If artificial intelligence is your main goal, compare the amount of required AI work with online university programs in AI before choosing either data science degree.

Consult each program's current admissions page for its degree, transcript, application, and preparation requirements; do not assume the two schools use the same prerequisites or application materials. In particular, verify whether programming, calculus, statistics, or prior professional experience is formally required, recommended, or simply helpful once enrolled.

Before applying, compare your preparation with the work in each program's first required courses. If you have a gap, ask admissions whether a course or professional project would strengthen your application - and whether it would satisfy a formal requirement. Self-study may improve readiness without replacing a required academic prerequisite.

Do not rely on an advertised completion time without checking its assumptions. Ask each school for a course sequence showing the standard number of courses per term, when required courses are offered, and the longest permitted time to finish. The realistic timeline for a working student may differ from an accelerated example, particularly if a required course is unavailable when needed.

Compare completion time only after estimating the weekly commitment you can sustain. If speed is your overriding priority and your interests extend beyond data science, artificial intelligence master's completion time comparisons provide another benchmark; a faster degree is not necessarily the better curricular fit.

Judge career preparation by the work you would finish, not by a promise attached to the degree. For each program, request recent examples of required projects and ask how students receive feedback on code, statistical choices, and explanations for nontechnical audiences. Also confirm which career advising, employer events, and alumni resources are available to online students.

This matters especially if you plan to change fields. A strong project should show your individual contribution, a defensible evaluation method, and a decision or product the analysis could inform. Neither school's name substitutes for evidence of those skills, and access to a career service does not guarantee a job.

For readers changing careers, online degree pathways into data careers offer a way to think through adjacent roles. The practical question is whether your intended program closes the gaps between your existing work and the entry requirements of those roles.

According to the U.S. Bureau of Labor Statistics, the median annual wage for data scientists was $120,230 in May 2025. That figure describes workers in an occupation across employers and experience levels; it is not the starting salary, salary increase, or expected outcome for graduates of either university.

Use salary information cautiously in a return-on-investment calculation. Estimate the roles you could realistically pursue, consider your current pay and experience, and compare likely education costs with several possible career paths - not a single national median. Ask both schools whether they publish online-program outcomes and, if so, how many graduates responded and whether the figures distinguish career changers from experienced professionals.

Choose Michigan MADS if its verified required projects, scheduling rules, and full cost best support the applied work you want to do. Choose Notre Dame if its verified coursework, learning structure, and full cost are a closer match. Without comparable current details, there is no evidence-based universal winner - and neither degree title should settle the choice.

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