2027 UT Austin MSDS vs. Washington Online MSIM Data Science Track: Cost, Curriculum, Admissions, and Format
Co-Founder and Chief Data Scientist
UT Austin's online Master of Science in Data Science (MSDS) and the University of Washington's online Master of Science in Information Management (MSIM) Data Science track lead to different kinds of graduate preparation. One centers the data science discipline; the other places data science within information management. For prospective students weighing technical depth against organizational context, the choice also depends on total cost, scheduling, and whether their background meets current admissions expectations.
This comparison identifies what is established, what needs confirmation before applying, and which trade-offs should drive the decision.
Key Things You Should Know
- UT Austin advertises approximately $10,000 in tuition for its online MSDS, before applicable fees and other expenses; obtain a current, itemized Washington quote before treating the price comparison as final.
- UT Austin is the more direct choice for a data science degree centered on quantitative and computational coursework. Washington's Data Science track sits within an information management degree, making the surrounding organizational curriculum an important part of its value.
- Neither degree promises a data scientist job. Compare required courses, available projects, admissions expectations, and the schedule you can realistically sustain - not degree titles alone.
How Do UT Austin MSDS and Washington Online MSIM Compare?
The defining difference is the degree you earn. UT Austin awards an MSDS; Washington awards an MSIM, even when a student pursues its Data Science track. That distinction matters if you want sustained technical study or, instead, to connect analytics with how organizations manage information.
This table compares the same decision criteria for both programs. Where a detail can vary by catalog or enrollment term, confirm it in the current program materials rather than assuming the track name settles it.
| Decision criterion | UT Austin online MSDS | Washington online MSIM, Data Science track |
| Degree awarded | Master of Science in Data Science | Master of Science in Information Management |
| Academic center of gravity | Data science methods and their computational and statistical foundations | Information management, with data science as a track |
| Published price reference | Approximately $10,000 advertised tuition, before additional costs | Request the current online-program tuition and mandatory-fee schedule |
| Best initial fit | Students seeking a focused technical data science curriculum | Students who want analytics alongside information and organizational decision-making |
| Key item to verify | Current course availability and total charges | Track requirements, total charges, and scheduling expectations |
Neither credential is inherently better. The relevant question is whether the courses you would actually take - and the work you could show afterward - match your target role.
Which Program Costs Less: UT Austin MSDS or Washington Online MSIM?
UT Austin has the clearer low-price proposition: its online MSDS advertises approximately $10,000 in tuition. That is a tuition figure, not a promise that every student's all-in cost will be $10,000. A reliable dollar-for-dollar verdict against Washington requires a current quote for the online MSIM Data Science track, including mandatory charges; an unverified or campus-program price would make the comparison misleading.
Request itemized estimates for tuition and mandatory fees from both programs, then add books or software, any required travel, financing costs, and the cost of taking time away from paid work. Ask whether tuition changes with course load or enrollment term and whether your employer will reimburse either degree. These details matter more than a comparison of headline prices alone.
If affordability is your binding constraint, UT Austin's advertised tuition makes it a strong starting point. Compare its full estimate with Washington's written quote and with other low-cost online data science master's programs before committing. A lower-priced program is not necessarily the better value if it lacks the coursework you need.
How Do the UT Austin and Washington Data Science Curricula Differ?
UT Austin's MSDS is organized around data science itself, including statistical and computational approaches to analyzing data. Washington's Data Science track is part of an information management degree. Its appeal is therefore not just the track's analytics courses, but how those courses connect to the broader study of information in organizations.
Look beyond the presence of "data science" in both program descriptions. Compare the current required-course lists side by side: How much work is devoted to statistics, programming, machine learning, data management, communication, and organizational decision-making? Check which advanced courses are required, which are electives, and whether an elective you want is available to online students during your expected enrollment period.
A course title alone cannot establish technical depth. Read descriptions and, where available, syllabi for the methods taught, programming expectations, assessments, and substantial project work. If you want a portfolio-ready project or a particular specialization, ask whether the program guarantees that opportunity or merely offers it when courses and supervision are available.
What Are the Admission Requirements for Each Data Science Program?
Check each program's current online-degree admissions page before planning an application. In particular, verify the accepted academic backgrounds, required application materials, language-proficiency rules where applicable, test policy, deadlines, and any stated mathematics or programming preparation. Do not assume that requirements for Washington's campus MSIM - or another UT Austin graduate program - apply to these online options.
Preparation should also be judged against the curriculum, not only the minimum application rules. For UT Austin, ask whether your evidence of programming, statistics, and quantitative problem-solving is strong enough for a technical MSDS. For Washington, show how your analytical skills and experience connect to managing or using information in organizations. A project, relevant coursework, or professional work can help explain readiness, but self-study does not automatically fulfill a formal prerequisite.
Applicants without a computer science degree need not treat their major as the entire admissions answer. They should identify specific skill gaps and confirm how each school evaluates them. A broader discussion of artificial intelligence graduate study for non-CS students may help frame preparation, but only each program can confirm its own admission rules.
How Do the Online Formats and Learning Experiences Compare?
Both are online options, but "online" does not answer the scheduling questions that can determine whether you finish. Confirm whether courses have live sessions, fixed meeting times, group-work requirements, timed assessments, or in-person obligations. Ask how often required courses run and what happens if work or family demands force you to skip a term.
UT Austin may suit someone who wants to concentrate their available study time on technical coursework. Washington may suit someone who values applying data science within information-management problems. Those are academic-fit judgments, not verified claims about which program is easier to schedule. If you work full time, compare sample term calendars and expected weekly commitments from both schools before choosing; advice on data science master's choices for full-time employees can help you test that plan against your work schedule.
How Long Do the UT Austin and Washington Programs Take?
Neither program has one completion time that applies to every student. Time to degree depends on the current credit and course requirements, how many courses you take each term, when required classes are offered, and any enrollment limits or approved breaks.
Ask both programs for a term-by-term plan based on your intended course load, not only their fastest advertised pathway. Have an advisor identify prerequisite sequences and any courses offered infrequently. A seemingly shorter plan is less useful if it requires a study load you cannot sustain while working.
What Careers Can Each Data Science Program Prepare You For?
UT Austin's technical focus aligns most directly with applications to roles involving statistical analysis, machine learning, and data science. Washington's combination of data science and information management may also appeal to students pursuing analytics roles that connect data work with information strategy, stakeholders, or organizational processes. Neither degree title by itself establishes eligibility for a particular job.
Start with several real job descriptions you would consider applying to. Compare their required tools and methods with each program's compulsory courses, then identify a project through which you could demonstrate the missing skills. For specialized interests such as clinical data work, domain knowledge and data-governance constraints matter alongside modeling; AI master's degrees relevant to clinical data work offer another comparison if that is your intended direction.
What Salaries Can Graduates Expect in Data Science Careers?
The U.S. Bureau of Labor Statistics reports a $120,230 median annual wage for data scientists in May 2025. This is a national occupational figure - not the median salary of UT Austin or Washington graduates, an entry-level offer, or an estimate of what either degree will add to your earnings.
For a personal return-on-investment calculation, compare your current compensation with plausible offers for roles you can realistically pursue after graduation. Account for your experience, location, employer, job level, and total degree cost. Without comparable, program-specific graduate outcomes, it would be misleading to use the national median to claim that one program produces higher salaries.
What Is the Job Outlook for Data Science Graduates?
The Bureau of Labor Statistics projects 35% growth in U.S. data scientist employment from 2025 to 2035. That signals expanding demand for the occupation, but it does not measure openings reserved for new master's graduates or the hiring prospects of either school's students.
Demand also does not remove the need to demonstrate skills. As tools and employer expectations evolve, evaluate whether your chosen curriculum gives you durable foundations and opportunities to apply them to real problems. Treat the outlook as context for the career decision, not as evidence that either degree guarantees a job.
How Should You Choose Between These Data Science Programs?
Choose UT Austin as your leading option if you want the MSDS credential, prioritize a focused technical curriculum, and its current courses cover the methods you need. Its advertised tuition is also a compelling reason to investigate it first if cost is decisive.
Choose Washington as your leading option if an MSIM is the credential you want and you would use the Data Science track within a broader information-management education. Confirm that the specific online courses, schedule, and full cost support that goal before paying an enrollment deposit.
A common mistake is to compare UT Austin's advertised tuition with an incomplete Washington price, or to assume either degree title guarantees particular skills or outcomes. Get written cost estimates, mark the required courses against target-job requirements, and request realistic study plans. If access to peers and graduates matters to your career strategy, ask both schools what online students can actually use; guidance on artificial intelligence master's alumni communities can help you formulate those questions without assuming access is identical across programs.
Other Things You Should Know About Data Science
UT Austin awards an MSDS. Washington awards an MSIM; ask Washington how the track appears on the transcript or diploma if that wording matters to you.
Possibly, if your employer's policy covers the institution, degree type, and enrollment arrangement. Confirm eligibility, annual limits, and reimbursement conditions with your employer before enrolling.
Do not assume so. Ask each school which career services, library resources, advising appointments, and student events are available remotely.
Leave and re-enrollment policies are program-specific. Confirm how a break would affect course sequencing, access, tuition, and the time allowed to complete the degree.
References
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