2027 Notre Dame Online MS in Data Science vs. Pace Online MS in Data Science: Cost, Curriculum, Admissions, and Format

Both are university master's programs aimed at data science study, but the decision turns on the current degree plan and delivery rules, not the shared degree name. The table uses the same questions for each school; entries marked for confirmation should not be treated as established program differences.

Bottom line: choose the program whose required coursework and schedule serve your intended role at a total price you can fund. University reputation can matter to an individual employer, but it cannot substitute for checking those four criteria.

A reliable side-by-side total requires each school's current tuition rate, required credits, and mandatory fees for the same enrollment period. A current, comparable total for these two programs cannot be established here, so quoting a precise tuition gap would be misleading. Ask both schools for an itemized estimate covering the entire degree, not just the first term.

Use the same calculation for each: required credits × applicable tuition per credit + mandatory program and university fees + required travel or campus costs. Then budget separately for books, software or hardware, financing charges, and income lost if you reduce working hours. Confirm whether tuition can change between terms and whether employer assistance, scholarships, or aid will actually apply to your enrollment.

Do not compare one school's tuition-only figure with the other's tuition-and-fees estimate. If both quotes exceed your budget, affordable online machine learning master's options can provide a different price benchmark, though their narrower curricula may not meet the same goals.

For both schools, use the application requirements for the online MS in Data Science and your intended entry term. Do not assume that a university-wide graduate admissions page establishes the program's mathematics, coding, test, or work-experience requirements. Current program-specific thresholds and document lists should be confirmed directly rather than inferred.

Ask each admissions office whether your transcript demonstrates the expected preparation in statistics, mathematics, and programming; which materials are required; whether tests are required or optional; and whether deficiencies can be addressed before or after admission. A personal project or professional experience may demonstrate readiness, but it does not automatically satisfy a formal course prerequisite. Request a written answer if you are relying on an exception.

The meaningful distinction is what each student must complete - not whether both program descriptions mention machine learning. Obtain both current degree maps and compare required courses, electives, and the final project or capstone. Course titles alone cannot establish how much coding, mathematical modeling, or implementation a student actually does.

For a machine-learning engineering goal, look for assessed work in model evaluation, data pipelines, and deployment. For analytics leadership, examine statistical reasoning, communication, and projects involving decisions and stakeholders. Ask to see course descriptions and, where available, examples of permitted project work. If your target is specifically AI rather than broad data science, use artificial intelligence degree selection online to test whether a specialized degree would be a closer match.

Neither program's completion time should be reduced to a single number without checking the student's course load and the school's current sequence. Ask each program for a term-by-term plan at the pace you can sustain, including when required courses are offered, whether summer enrollment is needed, and the maximum time allowed to finish.

A shorter advertised timeline is useful only if you can take the assumed course load while working. Compare the expected weekly commitment and any group-project or fixed-meeting obligations before selecting a faster route. If speed is your overriding priority and AI is an acceptable alternative subject, AI graduate degree options by completion time offer another comparison - but verify their schedules just as carefully.

"Online" does not, by itself, mean self-paced or free of campus visits. For each program, verify whether classes meet live, whether attendance is recorded, whether exams are scheduled, and whether any orientation, residency, presentation, or other campus activity is required. Also ask whether campus-based courses are available to online students or belong to a separate degree track.

This is especially consequential if you work shifts, live outside the institution's time zone, or cannot travel. Get the attendance and travel rules in writing before paying a deposit; an appealing online curriculum may be impractical if a required activity conflicts with your job or budget.

The University of Notre Dame is institutionally accredited by the Higher Learning Commission, and Pace University is institutionally accredited by the Middle States Commission on Higher Education. Institutional accreditation applies to the university; it should not be confused with separate, program-specific accreditation for an MS in Data Science.

Before enrolling, confirm each institution's current standing with its accreditor. If you will study from another state, ask the university whether it can enroll you from your location. Accreditation and permission to serve a student in a particular state answer different questions.

Depending on their previous experience and the projects they complete, students may pursue data scientist, data analyst, machine-learning, or analytics-focused roles. Those titles cover different work: an analyst may emphasize reporting and decisions, while a machine-learning role may require stronger software and production-model skills. Neither degree title alone establishes readiness for every role.

Compare actual course assignments with job descriptions in your target field, then choose projects that demonstrate the missing skills. Healthcare, for example, may place added importance on sensitive-data handling and domain context; students considering that path can compare AI master's options for healthcare rather than assuming a general data science degree is the only route.

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 the starting salary or measured outcome of graduates from Notre Dame or Pace. Prior experience, role, employer, industry, and location can make an individual offer substantially different.

BLS projects 35% employment growth from 2025 to 2035 for data scientists. Growth suggests opportunity, but it does not guarantee that either degree will produce a job or salary increase. Ask each program for any graduate outcomes it publishes, including the survey period, number of respondents, and whether salaries reflect experienced professionals. To assess how a target sector might affect your plans, consider industry choices and data science earning potential alongside your own job-market research.

Choose Notre Dame if its confirmed course plan, schedule, and full cost better support your target work; choose Pace if its confirmed combination does. Without comparable current quotes and degree maps, a claim that one is cheaper, faster, or technically deeper would be premature.

Before applying, request a complete degree plan, an itemized cost estimate, and written answers about attendance and travel from each school. Match the required assignments to several jobs you would realistically seek. The common mistake is deciding on reputation or an advertised tuition rate first, then discovering that the required coursework, total bill, or meeting schedule does not fit.

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