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

The clearest verified distinction in the programs' names is the credential: Notre Dame offers an online MS in Data Science, while Syracuse offers an online MS in Applied Data Science. Neither title tells you how many advanced modeling courses you must take or whether you can study at your preferred pace.

The comparison below keeps those questions separate from what the names suggest.

Use the same entry term when requesting information. A comparison built from one university's current tuition schedule and the other's older marketing estimate can produce a false winner.

A current, comparable total for the two programs cannot be established without each university's tuition rate, required credit count, and mandatory fees for the same enrollment period. Do not treat an unverified per-credit figure as a program price. Ask both financial aid offices for an estimate based on your intended course load and start term.

For each estimate, separate tuition from mandatory university or program fees, books and software, and any required travel. Then account for financing costs and reduced work hours if either applies to you. Employer tuition assistance can change your out-of-pocket cost substantially, but reimbursement rules may require minimum grades or continued employment.

Ask each school which institutional scholarships, graduate loans, and payment plans are available to online students, and whether aid depends on enrollment intensity. A common mistake is to compare sticker prices while overlooking fees or assuming an employer will reimburse a course before checking its policy. A lower-priced degree is not automatically the better investment if its required courses do not support your target work.

The meaningful comparison is the required curriculum, not a catalog of electives you may never be able to take. For each program, mark which courses require coding, statistical inference, machine learning, data engineering, communication with decision-makers, and a substantial independent or team project. Then check prerequisites and whether desired electives run during your likely enrollment window.

If you want research-oriented modeling or a technically intensive role, examine the mathematics, model evaluation, and computing expected in required assignments. If you want to put models into use in an organization, examine data preparation, deployment or implementation, stakeholder communication, and project scope. Neither program's name proves it is stronger on any of these measures.

Applicants whose primary goal is building AI systems should compare these course descriptions with those in explore artificial intelligence degrees online. That broader comparison is useful only if the data science curricula lack the AI depth you need.

Consult the current application checklist for your intended start term at each university. A bachelor's degree, transcripts, recommendations, a résumé, an essay, standardized tests, and prerequisite coursework should each be treated as a program-specific question, not assumed requirements shared by both schools. Application policies can change by term.

Pay particular attention to how each program evaluates readiness in programming, calculus, linear algebra, and statistics. If your transcript does not show a skill you use at work, ask whether a professional project can demonstrate it and whether any missing coursework must be completed before enrollment. Preparation that helps you succeed is not necessarily a formal admission prerequisite.

If preparing a statement is a significant application constraint, review essay-free application options for an ML master's rather than assuming either of these programs waives one. Confirm the actual checklist before paying an application fee.

A reliable completion-time comparison requires more than a published "as few as" timeline. Ask each program for a term-by-term plan showing required courses, their availability, and the workload for a pace you can sustain. Confirm whether classes meet live, whether recordings substitute for attendance, and whether exams, group projects, residencies, or other activities have fixed times.

This matters most if you work full time or live outside the university's time zone. A program with a convenient nominal duration may be a poor fit if required live meetings conflict with your job. Ask how a missed term affects course sequencing and the maximum time allowed to finish. For a broader scheduling comparison, see graduate data science study while working.

Neither school can be declared the stronger career choice from the credential name alone. For a target role, compare what students must produce: reproducible analyses, evaluated models, documented code, presentations, and projects based on realistic constraints. Ask to see recent project descriptions and whether students may work with an employer or an external client.

Also ask each program what career services online students can use and whether published employment figures specifically identify graduates of that online degree. A university-wide salary figure would not answer that question.

If access to peers and experienced practitioners matters to your transition, compare documented mentoring and alumni opportunities; graduate AI programs with active alumni communities offer a useful point of comparison for applicants considering an AI-focused alternative.

Depending on prior experience and demonstrated skills, relevant targets can include data scientist, machine learning practitioner, analytics specialist, or data-focused product and business roles. The degree does not qualify every graduate for every title: employers may expect different levels of software engineering, statistical modeling, or domain expertise.

BLS projects 35% growth in U.S. data scientist employment from 2025 to 2035. That indicates national demand for the occupation, not a placement forecast for Notre Dame or Syracuse graduates.

Compare each curriculum's projects with actual job descriptions in your intended market. For help mapping study to possible roles, see online data science programs and employment pathways. 

Notre Dame is institutionally accredited by the Higher Learning Commission, and Syracuse is institutionally accredited by the Middle States Commission on Higher Education. Institutional accreditation concerns the university; it should not be described as a separate, data-science-specific accreditation of either degree.

Before enrolling, check each accreditor's current directory and ask the university whether the online program is available to students residing in your state. Accreditation and permission to enroll from a particular state are different questions.

Do not assume that either degree requires a data science job title or a professional certification. Confirm formal requirements with admissions, then assess whether you can comfortably work with code, quantitative reasoning, and unfamiliar datasets at the pace required. Experience in another field can be valuable when it helps you define consequential problems or interpret results.

Certifications may demonstrate focused learning, but they should not be treated as substitutes for required academic prerequisites unless the program explicitly accepts them. If your background is light on programming or statistics, ask for examples of first-term assignments and complete a small data project before committing. That test is more informative than a certificate alone.

Choose Notre Dame if its confirmed required courses, schedule, projects, and net cost better support your target role. Choose Syracuse if its confirmed version of those same factors fits better. The "applied" label may prompt a useful question about hands-on work, but it is not evidence that Syracuse has more of it, or that Notre Dame has less.

Before accepting either offer, request a written total-cost estimate and a realistic course plan for your intended pace. Compare two or three required assignments or project descriptions against jobs you would actually pursue, and verify any aid, live-attendance, and state-enrollment conditions that could change your decision.

Do not choose solely on brand, a fastest-possible timeline, or an occupational salary figure that is not a program outcome.

• Semester Course Load and Student Performance - PMC - NIH https://pmc.ncbi.nlm.nih.gov/articles/PMC7568764/
• 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/
• Applied AI and Data Science Program | mit.edu https://professional.mit.edu/course-catalog/applied-ai-and-data-science-program
• Prerequisite Key Topics - UVA School of Data Science https://datascience.virginia.edu/pages/prerequisite-key-topics
• Practicum and Capstone: COPHP MPH | Health Systems and Population Health https://hspop.uw.edu/cophpmph/program-experience/practicum-capstone/
• Data Science | Academic Catalog | The University of Chicago http://collegecatalog.uchicago.edu/thecollege/datascience/