2027 UT Austin MSDS vs. Syracuse Online MS in Applied Data Science: Cost, Curriculum, Admissions, and Format
Co-Founder and Chief Data Scientist
UT Austin's online Master of Science in Data Science (MSDS) and Syracuse University's online MS in Applied Data Science offer different trade-offs for working professionals. UT Austin is the lower-cost, more flexible option; Syracuse emphasizes live instruction and applied work.
This comparison focuses on the costs to verify, the preparation each program calls for, and the learning experience you would be committing to - so you can choose based on your budget, schedule, and career goals rather than the degree names alone.
Key Things You Should Know
- Cost: UT Austin's widely advertised tuition benchmark is about $10,000 for its 30-credit MSDS. Obtain a current written total-cost estimate for both programs before treating that figure as your final bill.
- Format: UT Austin centers on asynchronous coursework; Syracuse emphasizes live online classes. The deciding question for many working students is whether scheduling flexibility or real-time interaction matters more.
- Fit: UT Austin is a strong option for students seeking a lower-cost, technically oriented degree. Syracuse may better suit students who value scheduled instruction and applied, collaborative learning - but neither degree guarantees a particular job.
How do UT Austin and Syracuse Online compare for data science degrees?
Both are online master's degrees, but they should be compared as learning experiences, not just as credentials. The table highlights the differences most likely to affect whether a student can finish the program and use what they learn.
| Decision factor | UT Austin MSDS | Syracuse online MS in Applied Data Science |
| Academic emphasis | Statistics, computing, and data science methods | Applied analysis, communication, and organizational use of data |
| Class experience | Primarily asynchronous learning | Live online instruction alongside work completed outside class |
| Budget starting point | Widely advertised tuition of about $10,000; verify current charges | Request a current, individualized tuition-and-fee estimate |
| Likely best fit | Independent learner prioritizing cost and schedule control | Learner prioritizing real-time discussion and structured participation |
These are differences in emphasis, not a ranking of academic quality. If your interests lean more toward business reporting than statistical modeling, a broader data analytics master's program comparison can also help you test whether either data science degree is the right category of program.
Which program costs less: UT Austin MSDS or Syracuse Applied Data Science?
UT Austin is the lower-cost starting point based on its advertised tuition. A fair final comparison, however, requires a current estimate from each school using the same definition of total cost. The available price information is not equally specific:
- UT Austin MSDS: Approximately $10,000 in widely advertised program tuition; confirm the tuition and any required charges for your enrollment period.
- Syracuse online MS in Applied Data Science: Obtain the current per-credit rate, required credit total, and mandatory-fee schedule directly from Syracuse before calculating a program total.
For each school, separate tuition from mandatory fees, books or software, financing costs, and any expense tied to your own circumstances. Then ask whether employer reimbursement, scholarships, or payment arrangements reduce what you would personally pay. Financial assistance pathways for a data science master's can help you identify questions to raise with financial aid staff.
A common mistake is comparing UT Austin's advertised tuition with a Syracuse estimate that includes fees - or assuming a lower sticker price automatically means a better return. Your relevant comparison is the amount you expect to pay for a curriculum and format you can realistically complete.
What are the admission requirements for each data science master's program?
Applicants to either program should expect to document prior education and explain their readiness for graduate-level data science. The distinction to investigate is how much mathematical and programming preparation each program expects at entry, rather than assuming professional experience or a degree title settles eligibility.
For UT Austin, review the current MSDS application instructions and assess your preparation in programming, probability and statistics, and foundational mathematics against the program's stated expectations. For Syracuse, review the online applied data science admissions instructions for the required application materials and any stated preparation expectations. A recommended skill is not necessarily a formal prerequisite; likewise, self-study may improve readiness without satisfying a required course.
Before applying, compare the current policies on transcripts, recommendations, statements or essays, standardized tests, and application deadlines. If an essay requirement is a deciding factor, check each school's own instructions rather than relying on a general list of no-personal-statement machine learning master's programs: those are different degrees, and application rules can change. Applicants with uneven preparation should ask admissions staff which specific prior courses or experiences can demonstrate readiness.
How do the online formats differ between UT Austin and Syracuse?
UT Austin's asynchronous model gives students more control over when they engage with course material. That can help people with changing shifts or substantial time-zone differences, but it demands consistent self-management. Check the current syllabus for deadlines, exams, and any scheduled activities; asynchronous does not mean deadline-free.
Syracuse's live online classes provide scheduled opportunities to ask questions and work with others. That structure can be valuable if discussion helps you learn, but recurring class times may be difficult to protect around work or caregiving. Before committing, ask for the expected meeting schedule in your time zone, attendance rules, group-work expectations, and the policy for missed sessions.
Neither format is automatically more rigorous. The practical test is whether you can sustain its weekly routine through demanding quantitative courses, not whether "online" sounds convenient.
What courses and specializations does each program offer?
UT Austin's MSDS is oriented toward statistical and computational foundations and their use in data science. Syracuse's applied data science degree places greater visible emphasis on using analysis in organizational settings. Course offerings and elective availability can change, so compare the current catalogs and course schedules - not just program-level descriptions.
To judge depth, look beyond labels such as "AI" or "machine learning." Check whether your target subject appears in a required course, an available elective, or a substantial assessed project. For technical data scientist roles, compare the work required in statistics, programming, model evaluation, and data management. For analytics roles that involve presenting findings to decision-makers, examine opportunities to practice communicating results and working through applied problems.
Ask both programs which electives will actually be offered while you expect to enroll and whether a capstone, portfolio, or other culminating project is required under your catalog year. Do not assume that an appealing course title guarantees a particular project or specialization.
How long do the UT Austin and Syracuse data science programs take?
UT Austin's MSDS has a 30-credit academic requirement; completion time depends on how many courses you take per term and when required courses are available. Syracuse's timeline likewise depends on its current credit requirements, course sequence, and the pace you choose. Neither school's promotional completion estimate should be treated as a promise that the same schedule will work for you.
Request a term-by-term plan for your intended start date. Check whether the courses you need are offered when you need them, whether part-time enrollment changes the sequence, and how long you may take to finish. If speed is a primary goal, data science master's duration and tuition comparisons provide a wider benchmark; confirm any candidate program's current schedule before making plans around it.
Are UT Austin and Syracuse data science degrees accredited?
UT Austin and Syracuse University hold institutional accreditation through their respective recognized institutional accreditors. That is the relevant starting point for checking the standing of these university-awarded degrees; it is not a claim that either specific data science master's has separate, specialized accreditation.
Before enrolling, confirm each institution's current accreditation status with the university and its accreditor. Also verify any state authorization rules that apply where you will study. Institutional accreditation and permission to offer an online program in a particular location answer different questions.
What careers can each data science master's program prepare you for?
Either degree can support preparation for data-focused roles, but the strongest match depends on the work you want to demonstrate to employers. UT Austin may appeal to applicants targeting technically intensive modeling or quantitative analysis. Syracuse may appeal to applicants who want to pair analytical methods with collaborative, stakeholder-facing work. Actual eligibility for a role will also depend on prior experience and the projects you can show.
Compare the programs against several real job descriptions that interest you. Identify the required tools and methods, then check which courses and projects would let you produce credible evidence of those skills. If your goal is a specialized domain such as healthcare, artificial intelligence graduate study for healthcare applications offers a more focused comparison; neither general data science degree should be assumed to provide healthcare-specific training.
What salaries and job prospects can data science graduates expect?
The U.S. Bureau of Labor Statistics reports a $120,230 median annual wage for data scientists in May 2025. That is an occupation-wide figure, not an entry-level offer or an outcome attributable to either university. Your pay may differ substantially by experience, responsibilities, location, and employer.
BLS projects data scientist employment to grow 35% from 2025 to 2035. The outlook supports interest in the field, but growth does not remove competition for individual positions. Evaluate either degree against the skills in your target jobs and the total cost you would incur; do not use the occupational median as a promised post-graduation salary or a ready-made return-on-investment calculation.
Which program is better for your data science career goals?
Choose UT Austin if its current total cost fits your budget, you want substantial technical study, and you can keep up with largely asynchronous work. Choose Syracuse if live interaction and an applied learning environment are worth the potentially different cost and scheduling commitment to you. There is no universal winner: the less expensive program is poor value if its format prevents you from progressing, while a more interactive program must justify its full cost for your goals.
Before accepting an offer, get current written answers to three questions from each school: What will I pay through completion? Which required and relevant elective courses can I take on my intended schedule? What attendance and project commitments will I need to meet? Compare those answers alongside your target job descriptions. This avoids the most consequential selection mistake - choosing on price, reputation, or a course list without confirming the program you can actually attend.
Other Things You Should Know About Data Science
Do not assume they will. Ask the receiving program to evaluate the specific courses and confirm its graduate transfer-credit limits before enrolling with a transfer plan.
Confirm the exact diploma and transcript wording with each university's registrar. Program marketing is not a substitute for the institution's credential policy.
Ask the program directly. Enrollment eligibility, access to course services, and scheduled class times may depend on your location and circumstances.
Check the current technical requirements before purchasing anything. Ask whether required software or computing resources are provided and whether your existing computer meets course needs.
References
- Cost of Attendance (Budget) | 2025-2026 Federal Student Aid Handbook https://fsapartners.ed.gov/knowledge-center/fsa-handbook/2025-2026/vol3/ch2-cost-attendance-budget
- Education credits: Questions and answers | Internal Revenue Service https://www.irs.gov/credits-deductions/individuals/education-credits-questions-and-answers
- Accreditation in the U.S. | ed.gov http://www.ed.gov/laws-and-policy/higher-education-laws-and-policy/college-accreditation/accreditation-in-the-us
- Financial Aid Glossary | California Student Aid Commission http://www.csac.ca.gov/financial-aid-glossary
- How to Become a Data Scientist | Harvard SEAS https://seas.harvard.edu/news/how-become-data-scientist
- 12 Data Science Skills (+10 Hard-Won Career Lessons) | Syracuse University https://ischool.syracuse.edu/data-science-skills/
- Key Financial Aid Terms | passhe.edu https://www.passhe.edu/students/financial-aid-terms.html
- MSBA Admissions | UCSD Rady School of Management https://rady.ucsd.edu/programs/ms-business-analytics/admissions/
