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

The strongest comparison is between the coursework and experience you would actually complete, rather than the words "applied" and "science." The table identifies the decision points that deserve a side-by-side check before you apply.

Neither credential is a universal winner. A better fit is the program whose required work develops the skills you lack, whose schedule you can sustain, and whose full cost you can finance without relying on a hoped-for salary increase.

The University of Michigan and Grand Canyon University are both institutionally accredited by the Higher Learning Commission. Institutional accreditation concerns the university as a whole; it is not evidence that either master's program holds a separate data-science accreditation. For this comparison, accreditation is therefore not an obvious differentiator.

Before enrolling, confirm that the specific program is covered under its university's current accreditation and ask whether your state of residence affects enrollment eligibility. Accreditation, state authorization, and eligibility for a particular employer's tuition benefit are distinct questions.

Michigan's MADS explicitly frames the credential around applied data science. Grand Canyon names its degree an MS in Data Science. Those labels suggest different emphases, but they do not verify how much statistics, machine learning, software development, or practical project work either student must complete. The current required-course lists and course descriptions - not the program names - should settle that question.

Compare whether both degrees require substantial work in statistical reasoning, programming, data preparation, model evaluation, and communicating results. If building data pipelines is central to your target role, inspect assignments and project requirements rather than assuming a course that mentions databases provides data-engineering depth. You can also review data engineering content in AI master's programs as a separate benchmark; those are alternative programs, not evidence about either degree here.

Ask each department for a current degree plan and an example of the work expected in its final project, if one is required. A course title alone cannot show how independently students must work with messy data or evaluate a model.

Both applicants need to check the current admissions page for their specific degree. A university-wide graduate admission policy may not capture a program's expectations for coding, mathematics, transcripts, recommendations, or professional experience. Do not assume that either a computer science bachelor's degree is required or that any bachelor's degree is sufficient without reviewing the applicable policy.

If your background is outside computing, ask each admissions team to distinguish a formal prerequisite from preparation they recommend for success. Provide evidence of programming, statistics, or quantitative projects where relevant, and ask whether self-study or professional work can demonstrate readiness. If you are also considering a different credential, you can compare AI master's options for non-CS backgrounds; their admission rules should not be assumed to apply to Michigan or Grand Canyon.

The better format is the one that fits your weekly availability, not simply the one described as online. Ask both programs which activities are asynchronous, whether live meetings are required, how group work is scheduled, and whether assessments have fixed times. Also check for any on-campus requirement before treating either option as entirely location-independent.

This matters most if you work shifts, travel, or live in a different time zone from classmates. Compare the course calendar with your work calendar, then ask how many courses students typically take while employed full-time. For additional alternatives, see data science graduate programs for working students; an alternative's scheduling flexibility does not establish how either program in this comparison operates.

A defensible completion estimate depends on the current credit requirement, when required courses run, and how many courses you can take each term. Do not treat a school's fastest advertised pathway as your likely timeline, particularly if you will remain employed.

Request a term-by-term plan from each university based on the same workload. Ask whether a required course is offered every term, whether courses must be taken in sequence, and whether there is a maximum time to finish. Those answers will show whether a seemingly flexible program can accommodate the pace you need.

A reliable 2026 price comparison requires current, program-specific tuition schedules and degree requirements for both schools. Without verified figures for those items, a precise total - or a claim that one degree is cheaper - would be misleading. Request an itemized estimate from each university covering tuition for every required credit, mandatory fees, and any required materials or technology charges.

Then calculate both estimates using the same assumptions about course load and completion time. Check whether tuition changes with residency, enrollment status, or academic year; whether an employer will reimburse this particular degree; and what additional terms would cost if you slow down. If the resulting total exceeds your budget, lower-tuition artificial intelligence degree options may be worth comparing, but an AI curriculum is not automatically a substitute for either data science degree.

Either degree may support applications for data scientist, analytics, or related technical roles, depending on the graduate's prior experience and demonstrated skills. A degree title does not establish readiness for every position: some jobs emphasize statistical modeling, while others require production software or data pipelines.

Use current job postings for your target role to identify the methods and tools employers request. Then compare those requirements with each program's required coursework and the projects you could show employers. This is especially important for career changers, who may need evidence of hands-on work alongside the credential.

The U.S. Bureau of Labor Statistics reports a $120,230 median annual wage for data scientists in May 2025. That is a wage for people employed in the occupation, not an expected starting salary or a reported outcome for graduates of either university.

BLS projects 35% employment growth for data scientists from 2025 to 2035. The projection signals demand for the occupation, but it does not predict an individual applicant's chances. Experience, location, technical skills, and the roles you pursue matter when assessing whether either degree is worth its cost.

Choose Michigan MADS if its current required courses and project expectations better match the applied skills you want to build, and its verified total cost and schedule work for you. Choose Grand Canyon's online MS if its degree plan better addresses your skill gaps and its confirmed format and full price make it the stronger practical fit. Neither recommendation follows from institutional reputation or degree title alone.

Before accepting an offer, obtain both degree plans, two itemized cost estimates, and two realistic schedules based on your intended course load. Ask to see the kind of project work students complete and what access you will have to faculty, career support, and graduates. If connections to practitioners are a priority, investigate each program's actual community rather than assuming it resembles artificial intelligence master's alumni communities at other schools. The most costly mistake is committing before checking whether the coursework, time demands, and complete price all fit your goal.

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• What Financial Aid Offers Don't Tell You About the Cost of College | U.S. GAO https://www.gao.gov/blog/what-financial-aid-offers-dont-tell-you-about-cost-college
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• MS in Data Science Curriculum & Course Descriptions | Eastern University https://www.eastern.edu/academics/colleges-seminary/college-health-and-sciences/departments/department-mathematical-5
• Data Science and Society (DATA) - UNC Catalog https://catalog.unc.edu/courses/data/
• Course Prerequisites and Corequisites | Curriculum Services | Kent State University https://www.kent.edu/provost/curriculum/course-prerequisites-and-corequisites
• Master of Applied Data Science | umsi https://www.si.umich.edu/programs/degree-programs/master-applied-data-science
• Admission Process - Data Science - University of North Texas https://datascience.unt.edu/programs/msds/admission.html
• Data Science | Academic Catalog | The University of Chicago http://collegecatalog.uchicago.edu/thecollege/datascience/
• Online MS in Data Science Degree | GCU https://www.gcu.edu/degree-programs/ms-data-science