2027 UT Austin MSDS vs. Pace Online MS in Data Science: Cost, Curriculum, Admissions, and Format

Use the same questions for both schools: What must you complete, what will you pay, and how will you study? The table identifies a useful starting point without treating an unverified Pace price or schedule as settled fact.

UT Austin has a clear advertised tuition benchmark. Pace could still be the better fit if its confirmed curriculum, pacing, or learning experience addresses a need UT Austin does not. Do not substitute either university's reputation for a course-by-course comparison.

UT Austin is the lower-cost candidate based on its advertised $10,000 tuition, but a defensible 2026 comparison requires Pace's current full-program quote and UT Austin's current charges. An advertised program price and a per-credit rate are not equivalent unless you account for every required credit and fee.

Ask each school for tuition, mandatory fees, expected materials or software expenses, and any charges tied to your enrollment pattern. Then subtract confirmed scholarships or employer reimbursement and consider borrowing costs. If you are comparing additional economical online data science master's options, apply that same calculation to every school.

A common mistake is to choose from a tuition figure alone. Pace's actual cost may depend on its current rate and your approved course plan; UT Austin's advertised figure should likewise be checked against the terms that apply when you enroll.

Compare required learning, not just course titles. UT Austin's MSDS is a compelling option for applicants who want substantial work in statistical reasoning and computation. For Pace, inspect the current catalog to determine how much of the degree is devoted to statistics, programming, data management, machine learning, electives, and applied projects.

For a modeling-focused career, examine the prerequisites and assignments in machine-learning courses: a course that requires implementing and evaluating models offers different preparation from one that primarily surveys tools.

Students primarily seeking that specialization can also compare budget-friendly ML master's programs under $20,000. For analytics roles, assess whether you will practice explaining findings, working with imperfect data, and delivering results to nontechnical stakeholders.

Ask both programs for recent syllabi or sample assignments. A substantial project you can discuss in interviews may matter more for your goal than an elective with an appealing name.

Both degrees are offered online, but "online" does not settle when you must attend, collaborate, or take assessments. UT Austin's online MSDS may appeal to students comfortable directing much of their study independently. Before choosing Pace for greater interaction, or UT Austin for greater flexibility, confirm what each course actually requires.

Request sample syllabi showing recorded versus live sessions, assignment deadlines, group-work expectations, exam arrangements, and instructor or teaching-assistant access. This is especially important if you work irregular hours or outside the program's usual time zone. Compare the support you would use, not merely the number of services a university lists.

Expect both applications to require evidence that you can handle graduate study, but check each program's current admissions page for its exact degree, transcript, recommendation, statement, test, and deadline policies.

For UT Austin in particular, assess your preparation in programming, calculus, and linear algebra before treating admission as a question of grades alone. Ask Pace which quantitative and programming skills it expects at entry and whether any preparatory coursework is formally required.

If you have a gap, distinguish preparation from eligibility. A course or independent project can strengthen your skills, but it fulfills an admissions prerequisite only if the school accepts it. Send an admissions adviser your transcript and a specific description of relevant work before paying an application fee.

UT Austin's 30-credit requirement provides a basis for planning, but completion time depends on how many courses you can take and when required courses are offered. Get Pace's current credit requirement and a term-by-term plan before comparing advertised completion times. A fastest-possible schedule may be unrealistic alongside full-time work.

Ask both programs for a sample plan at your intended course load, including course availability, breaks, and any maximum completion period. If your main goal is AI rather than a broader data science degree, compare the time commitment as well as tuition against budget-friendly online master's programs in AI; a different degree is useful only if its required work matches your goal.

Potential targets include data scientist, data analyst, machine-learning practitioner, and analytics-focused roles. The realistic next step depends heavily on your prior experience and project evidence: a software developer may be closer to a production modeling role, while someone with business-domain expertise may be well positioned for applied analytics.

Map the required courses and available projects at each school to actual job descriptions in your target industry. For a wider look at data science degrees and growing data occupations, keep in mind that an occupational trend describes the market, not placement results for either university. 

The U.S. Bureau of Labor Statistics reports a $120,230 median annual wage for data scientists in May 2025. That is a national occupational median, not a reported starting salary or earnings outcome for UT Austin or Pace graduates. Prior experience, location, employer, and the role secured can move an individual offer substantially above or below it.

Use salary estimates for the specific jobs you could plausibly pursue after graduation, then weigh any likely change in earnings against your full education cost. Do not calculate a degree's return by treating the national median as a promised postgraduation salary.

Check each university's current institutional accreditation status through the institution and its recognized accreditor before enrolling. Institutional accreditation is distinct from a claim that a particular data science master's has specialized program accreditation; ask for the specific accrediting body if either school makes that claim.

Data science is not generally a licensed profession in the way some clinical occupations are. Nevertheless, employers may expect responsible handling of sensitive data, sound model evaluation, and clear documentation. Review whether the courses and projects you would take give you opportunities to demonstrate those practices.

Choose UT Austin for closer consideration if its confirmed price fits your budget, its required quantitative and computing work matches your goal, and you are comfortable with its actual course-level schedule.

Choose Pace for closer consideration if its current curriculum, interaction, or pacing better addresses your needs, and the complete written cost is acceptable. Neither is a universal winner.

Before applying, obtain a full cost estimate and a sample course plan from each school. Compare two or three required-course syllabi with the skills in jobs you want, then ask how students receive feedback on projects.

If your goal is specifically AI work, use the same skill-based approach to matching an AI degree to your professional goals rather than assuming either data science degree is automatically the best route.

• 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
• Tuition & Financial Aid | MS in Data Science - Pace Online https://online.pace.edu/graduate-programs/ms-in-data-science/tuition-and-financial-aid/
• 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/
• Graduate Admission Requirements | University of Cincinnati Admissions https://www.admissions.uc.edu/apply/graduate/requirements.html
• What Is Data Science? Definition, Skills, Applications & More | Harvard SEAS https://seas.harvard.edu/news/what-data-science-definition-skills-applications-more
• FAQ | Computer & Data Science Online https://cdso.utexas.edu/faq
• Curriculum - MU Institute for Data Science and Informatics https://muidsi.missouri.edu/academic-programs/m-s-data-science-and-analytics-program/masters/curriculum/
• The Types of Courses We Offer - Harvard Extension School https://extension.harvard.edu/student-experience/course-formats/