2027 Washington Online MSIM Data Science Track vs. Grand Canyon Online MS in Data Science: Cost, Curriculum, Admissions, and Format
Co-Founder and Chief Data Scientist
The University of Washington's online MSIM Data Science track and Grand Canyon University's online MS in Data Science lead to differently named credentials. For prospective students choosing between them, that distinction matters - but it does not establish which program teaches more technical material, costs less, or fits a working schedule better.
This comparison separates what the degree titles tell you from the details you should confirm before applying, so you can judge each program against your intended role, budget, and available study time.
Key Things You Should Know
- UW awards an information-management master's with a Data Science track; GCU awards a master's in Data Science. Compare required courses and projects, not degree titles alone. Do not treat a tuition rate as a total price.
- Obtain a current, individualized estimate of all required credits and fees from each university before comparing costs.
- The U.S. Bureau of Labor Statistics reports a $120,230 median annual wage for data scientists in May 2025; that occupation-wide figure is not a salary outcome for graduates of either program.
How does the UW MSIM Data Science track compare with Grand Canyon's online data science master's?
The central difference is the credential's focus: UW's Data Science track sits within an MS in Information Management (MSIM), while GCU's degree is an MS in Data Science. That makes UW worth examining if you want to apply analytics within broader information-management work, and GCU worth examining if you want a degree explicitly named for data science. Neither title, by itself, proves greater technical depth or stronger hiring results.
This comparison identifies what is clear from the degree structures and what still needs a course-by-course check.
| Decision criterion | UW online MSIM, Data Science track | GCU online MS in Data Science |
| Credential | Master's in information management with a named data science track | Master's in data science |
| Question to test | How much of the required degree is devoted to statistical and computational work versus broader information-management study? | How far do required courses progress beyond introductory data science methods? |
| Best-fit possibility | Students seeking to connect data work with the management and use of organizational information | Students seeking a credential explicitly centered on data science |
| Unresolved comparison | Current required courses, schedule, and full price need confirmation | Current required courses, schedule, and full price need confirmation |
If your goal is specifically machine learning rather than a broader master's curriculum, compare both degrees with graduate machine learning programs under the $20,000 threshold. A narrower alternative could be more efficient, provided its required coursework and credential meet your goals.
What accreditation should students verify when comparing online data science master's programs?
Verify each university's current institutional accreditation directly in the accreditor's directory and the university's accreditation information. Check the institution named on the record, its current status, and whether the online program you intend to enter is offered by that institution. Do not assume that a data science track has separate, discipline-specific accreditation merely because its university is accredited.
Accreditation is a baseline check, not a measure of how much programming or statistics a degree teaches. If you will study from a different state, also ask each school to confirm that it can enroll students where you live. That enrollment question is distinct from accreditation and is worth resolving before paying an application fee.
What do UW and Grand Canyon require for admission to online data science master's programs?
Neither university's current application requirements, deadlines, or prerequisite rules should be assumed from the degree title. Before choosing where to apply, compare the current admissions pages for the specific online program and entry term. In particular, distinguish a course that is formally required for admission from knowledge the program recommends for succeeding after enrollment.
Ask each admissions team for a written answer on the points most likely to change your application plan:
- Which transcripts, recommendations, statements, résumé materials, or other documents are required for your intended entry term?
- Are programming, calculus, linear algebra, or statistics formal prerequisites, and can work experience or prior coursework satisfy them?
- Are standardized tests required, optional, or not considered for this specific online program?
- If a prerequisite is missing, must it be completed before admission or can it be completed alongside graduate study?
A common mistake is to self-reject because your bachelor's degree is not titled "computer science," or to assume professional analytics experience automatically waives a formal prerequisite. The published policy and an individual admissions review - not either assumption - should guide your next step.
How do the UW and Grand Canyon data science curricula differ?
UW's track structure creates an important question: how much room does the MSIM leave for data science after its broader degree requirements? GCU's degree title raises a different question: which data science methods are required, and which are merely available as electives? Answering both requires the current catalog, course descriptions, and a sample degree plan - not a list of promotional topics.
For each program, trace a path from foundations to advanced work and a substantial project. Look for explicit coverage of statistical inference, programming, data preparation, machine learning, model evaluation, and communication of results. Then check whether you can apply those skills to a realistic dataset. A topic mentioned in one course is not equivalent to a dedicated advanced course or a sustained project.
Curriculum fit also depends on your target industry. For example, students aiming at clinical or health-system applications may need domain-specific preparation beyond a general data science degree; graduate AI training for healthcare innovation careers offers a relevant alternative to examine. Ask both universities whether suitable electives and projects are actually available to online students when they plan to enroll.
What are the online learning formats at UW and Grand Canyon?
"Online" does not establish whether classes are asynchronous, whether live meetings are required, or whether exams and group work occur at fixed times. Those details may matter more than the platform to a student working full time. Request the current online-course schedule and a recent syllabus for a required course at each university.
Compare live-session times in your time zone, attendance and exam rules, group-project expectations, available course starts, and access to instructors and career support. If you need substantial control over weekly study hours, ask what happens when you cannot attend a live session rather than assuming a recording will count. Readers comparing other data science master's degrees for employed adults should apply the same scheduling test; an online label alone does not establish working-adult fit.
How long do the UW and Grand Canyon data science master's programs take?
A reliable completion estimate requires more than the number of courses. Ask each university for its current required credit total, credit system, course availability, permitted course load, and maximum time to finish. Then have an advisor map the sequence for your expected pace, including any prerequisites or culminating project.
A stated fastest completion time may depend on uninterrupted enrollment and a heavier load than you can manage. Conversely, a lighter load may not be available every term if required courses rotate. Compare UW and GCU using your likely enrollment pattern, not one school's fastest advertised route against the other's part-time plan.
How much do UW and Grand Canyon online data science master's programs cost?
A defensible UW-versus-GCU price comparison needs current, program-specific figures for the same scope of expenses. A verified total for either degree is not established here, so neither can responsibly be labeled the cheaper option. Ask each financial-aid or program office for a written estimate based on your entry term, residency or applicable tuition category, and expected enrollment pace.
Use the same cost categories for both quotes:
- Tuition: The rate applied to every credit required for your degree plan, including any required coursework outside the track or specialization.
- Required fees: Mandatory online, technology, graduation, or other program charges, identified separately from tuition.
- Situational expenses: Required software or materials, prerequisite courses, and any travel the program confirms is necessary.
- Financing and time: Potential borrowing costs and whether your study plan would reduce paid work; these depend on your circumstances.
Do not multiply a per-credit price by an unverified credit count or compare one university's tuition-only quote with the other's tuition-plus-fees estimate. If price is the deciding constraint and your goal is primarily AI training, lower-cost artificial intelligence degrees online are another category to compare - but check their curriculum before substituting one for a data science degree.
What careers can graduates pursue after earning an online data science master's?
Either degree may be relevant to data scientist and related analytics roles, but hiring depends on demonstrated skills, prior experience, and the job's requirements. UW's information-management framing may be especially relevant when you want to connect analytical work with information strategy or organizational decisions. GCU's data science degree title may communicate a more direct disciplinary focus. The stronger choice is the one whose completed coursework and projects give you evidence for your target job descriptions.
Before enrolling, collect several postings for the roles you actually want. Note the required tools, statistical methods, domain knowledge, and expectations for building or explaining models. Compare those requirements with each program's mandatory courses and the projects you can complete. This avoids the costly mistake of choosing by degree name while overlooking a skill gap that matters to your intended employers.
What salaries and job outlook can data science master's graduates expect?
The U.S. Bureau of Labor Statistics reports a $120,230 median annual wage for data scientists in May 2025 and projects 35% employment growth from 2025 to 2035. These are occupation-wide U.S. measures, not starting salaries, placement rates, or returns attributable to either UW or GCU. They indicate a growing field but cannot tell you what a particular graduate will earn.
For a personal return-on-investment decision, compare the full degree cost with a realistic next role based on your existing experience, location, and intended sector. Compensation can vary across industries; data science degree earnings by sector can help frame that question. If either university publishes graduate outcomes, check the survey population, response rate, role definitions, and whether reported pay reflects experienced professionals rather than new entrants.
Are certifications or licenses required for data science careers?
Data scientist is not generally a state-licensed occupation in the United States. Employers may still request particular software skills, cloud credentials, security clearances, or industry knowledge for individual jobs. Treat those as job-specific requirements, not a universal reason to buy an additional credential.
If postings in your target field repeatedly name a certification, ask whether either program prepares students for it and whether an exam fee is included. Otherwise, a well-explained project may provide more relevant evidence of your abilities than an unrelated certificate. Neither a certification nor either master's degree guarantees employment.
Other Things You Should Know About Data Science
Do not assume either answer. Ask each registrar for the exact diploma wording and whether the transcript identifies the delivery format, track, or specialization.
Only if your employer permits it and the university's project rules allow it. Ask about de-identified, public, or synthetic datasets before planning a project around restricted data.
Transfer-credit and prior-learning decisions are program-specific. Send each school your transcripts and course descriptions, then request a written evaluation before relying on credits to reduce time or cost.
Confirm the annual benefit limit, eligible programs, grade requirements, payment timing, and any repayment obligation if you leave the job. Those terms can change which offer is more affordable for you.
References
- Estimate your college cost | usa.gov https://www.usa.gov/estimate-college-cost
- UW Master of Data Science Program Overview | Trends and Issues in Higher Ed https://www.washington.edu/datasciencemasters/
- MSIM - Tuition, Aid & Scholarships - UW Information School https://ischool.uw.edu/programs/msim/tuition-financial-aid
- Is a Master's in Data Science Worth It? | GCU Blog https://www.gcu.edu/blog/engineering-technology/is-a-masters-in-data-science-worth-it
- MS in Data Science | gwu.edu https://datasci.columbian.gwu.edu/ms-data-science
- Data Science Specialization | Information School | University of Washington https://msimonline.ischool.uw.edu/msim-online/data-science-specialization/
- Synchronous vs. Asynchronous Learning | Saint Leo University https://www.saintleo.edu/about/stories/blog/synchronous-vs-asynchronous-learning-for-course-delivery
- What's the Difference Between Asynchronous and Synchronous Learning? | Ohio State Online https://online.osu.edu/content-hub/blogs/whats-the-difference-between-asynchronous-and-synchronous-learning/
