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

The central choice is not between "applied" and "theoretical" data science. It is between two specific sets of required courses, prices, admission rules, and scheduling commitments. The table separates what their degree names establish from the details an applicant needs to verify for a fair comparison.

Neither degree name establishes a winner. A student seeking a particular specialization should first identify the courses they would be required to complete - not just electives listed as available - and then weigh any difference against the full cost and schedule.

A reliable lower-cost verdict requires current, program-specific tuition and fee schedules for the same enrollment scenario. Without verified rates, required credit totals, and mandatory fees for both programs, stating a price difference would be misleading. Ask each school for an estimate based on your planned start term and pace.

The table shows what belongs in each estimate. It prevents the common mistake of comparing one school's tuition-only figure with another school's broader cost of attendance.

For financial planning, compare estimates at the same pace and ask how a tuition change or an extra term would affect each total. If borrowing is likely, paying data science master's tuition with financial aid is a separate decision from identifying the lower sticker price.

Compare the current catalogs at the required-course level. A useful data science curriculum review asks where students practice programming, statistical reasoning, data management, modeling, and communicating results - and whether those skills culminate in assessed work. An elective does not provide the same assurance as a required course.

The practical distinction to look for is depth, not terminology. For example, a program may introduce machine learning in one course, offer advanced study as an elective, or require students to apply it in a substantial project. Ask both schools for syllabi or recent course descriptions if the catalog does not make that distinction clear. If your goal is primarily business-facing analysis, comparing graduate data analytics degrees may also help you decide whether a data science degree is the right category of program.

Before choosing, identify the courses you would actually take under your intended enrollment plan. Check elective availability and project requirements rather than treating every course on a program webpage as guaranteed part of your degree.

Both degrees are presented as online options, but "online" does not answer the questions that determine whether a working adult can attend. The consequential differences are whether meetings are live, when they occur, how frequently courses begin, and how assignments and group work are scheduled. Do not assume either program is fully self-paced.

Ask Syracuse and Grand Canyon for a sample term schedule for the courses you would take first. Confirm any required live sessions, their time zone, expected weekly work, exam arrangements, and whether projects require coordinating with classmates. A predictable meeting time may suit someone who can protect that time each week; deadline-based study may better suit rotating shifts, provided the deadlines themselves are manageable.

Use each program's current graduate admissions page and application checklist, rather than a general university admissions page. Requirements that matter for this comparison include acceptable prior degrees, any mathematics or programming preparation, transcripts, recommendations, statements, testing policies, and application deadlines. A precise, current side-by-side requirement list cannot be established without the schools' applicable policies for your entry term.

If your prior degree is outside a quantitative field, do not assume you are either eligible or ineligible. Send each admissions team your transcript and a concise description of relevant coursework or work experience, then ask whether you meet formal admission requirements and whether they recommend additional preparation. Recommended preparation is not necessarily an admissions prerequisite.

Completion time depends on each degree's required credits, course sequencing, term calendar, and how many courses you can take while working. A school's fastest advertised timeline may assume a heavier course load than you intend. Without a verified plan of study for the same enrollment pace at both schools, a single "faster program" claim would not be dependable.

Request a term-by-term plan from each program showing your intended start date, any courses offered only in particular terms, and the earliest realistic graduation date at your chosen pace. If finishing quickly is your overriding priority and your interests extend beyond data science, compare the actual course loads of accelerated AI master's program options rather than relying on advertised completion times alone.

Syracuse University holds institutional accreditation from the Middle States Commission on Higher Education, while Grand Canyon University holds institutional accreditation from the Higher Learning Commission. Institutional accreditation applies to the university; it should not be described as a separate, data-science-specific accreditation of either master's program.

Before enrolling, confirm each institution's current standing with its accreditor and ask the school about any accreditation or approval claim that matters to your employer or funding source. Accreditation is an important baseline check, but it does not establish that two curricula, prices, or graduate outcomes are equivalent.

Depending on their prior experience and the skills demonstrated in their coursework and projects, graduates may pursue data scientist, data analyst, analytics, or related modeling roles. More engineering-intensive jobs may additionally expect substantial software development, deployment, or data infrastructure experience. Neither degree title alone establishes readiness for a particular job.

Work backward from several real job descriptions in your target industry: note the tools, statistical methods, and project evidence employers request, then check which program's required work lets you demonstrate them. For applicants targeting clinical or health-related AI work, master's-level AI study for health-related roles offers a useful adjacent comparison; specialized roles can require domain knowledge beyond a general data science curriculum.

The US Bureau of Labor Statistics reports that the median annual wage for data scientists was $120,230 in May 2025. That is a national occupational median across workers with varied education and experience - not a starting salary, a salary increase caused by a master's degree, or an outcome specific to Syracuse or Grand Canyon.

BLS projects data scientist employment to grow 35% from 2025 to 2035. Strong projected growth does not remove competition for individual openings: relevant experience, project quality, location, and employer requirements still matter. Neither school's graduate salary or placement rate should be assumed from national BLS figures; request program-specific outcomes and their survey methodology if those results are important to your decision.

Choose Syracuse if its confirmed required courses and online schedule better match the skills you need, and its full cost is workable for you. Choose Grand Canyon if its confirmed curriculum and schedule fit better at a total cost you can justify. On the information established here, neither is a defensible universal winner - and neither should win solely because of its degree title.

Before paying an enrollment deposit, complete the same short check for both schools. It turns broad marketing claims into a decision based on your circumstances.

1. Get a written estimate for required tuition and fees based on your intended pace, then account for materials, aid, and likely borrowing.
2. Mark the required courses and projects that produce evidence for your target job; distinguish them from optional electives.
3. Obtain a term-by-term schedule and confirm live attendance, deadlines, and expected completion time.
4. Have admissions confirm your eligibility and any preparation they expect before you enroll.

If neither curriculum matches a strongly technical machine-learning goal within your budget, consider online graduate ML programs with lower total tuition as an alternative - not as evidence that either program compared here lacks value. The avoidable mistake is committing before you know what you will study, when you must study it, and what you will pay.

• Types of Financial Aid: Grants, Work-Study, and Loans | studentaid.gov https://studentaid.gov/understand-aid/types
• 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 costs - Syracuse University https://www.syracuse.edu/admissions-aid/cost/graduate-costs/
• Online and Evening Tuition Rates | Grand Canyon University https://www.gcu.edu/tuition/online-evening
• Data Science | Academic Catalog | The University of Chicago http://collegecatalog.uchicago.edu/thecollege/datascience/
• Curriculum - MS in Data Science - Pace University Online https://online.pace.edu/graduate-programs/ms-in-data-science/curriculum/
• Master of Applied Data Science | Online & On-Campus Program | Syracuse University https://ischool.syracuse.edu/academics/applied-data-science-masters-degree/
• Tuition & Aid | Syracuse University iSchool Online https://onlinegrad.syracuse.edu/information-science/admissions/tuition-financial-aid/
• Statistics Data Science Curriculum | stanford.edu https://statistics.stanford.edu/academic-programs/graduate-programs/statistics-data-science-curriculum