2027 Boston University Online MSDS vs. Syracuse Online MS in Applied Data Science: Cost, Curriculum, Admissions, and Format

Both are online master's options for studying data science. The consequential differences are the specific courses available to you, what you must demonstrate to graduate, the time you must reserve each week, and the price on your enrollment offer. The table frames an equivalent comparison without treating an unverified program detail as fact.

A good decision starts with the same proposed start term and a realistic course load at each school. Otherwise, a fast-path estimate at one university can look more attractive than a part-time plan at the other without reflecting the experience you would actually have.

A current, comparable total price for both degrees cannot be established here, so neither program should be labeled the cheaper option. Ask each university for a written estimate for your intended start term that separates tuition, mandatory university and program fees, and any required software or materials. Confirm whether its quoted rate can change while you are enrolled.

If one school quotes a per-credit rate, multiply it by the credits you must complete, then add required fees. Compare that figure with the other school's similarly defined total - not an advertised tuition figure that omits fees. Also account for interest if you borrow, and subtract only employer assistance or scholarships you have confirmed you can use. For a broader price benchmark, review affordable paths to a data science master's degree.

The common mistake is choosing from sticker prices before checking whether a lower-cost schedule would require more time away from work or whether employer reimbursement applies to one program but not the other. Those circumstances can change your personal net cost without changing either university's published tuition.

The verified distinction in the program names is that Syracuse calls its degree Applied Data Science; the word "applied" is not evidence, by itself, of more projects or less technical depth. For an accurate comparison, request the current degree requirements and course descriptions from both schools for your entry term. Count required coursework separately from electives you might not be able to fit into your plan.

Pay particular attention to statistics and probability, programming and data engineering, machine learning, model evaluation, and the final project or other culminating requirement. A course title mentioning AI does not establish that students build, test, and explain models. This distinction matters if you want portfolio evidence for technical hiring rather than exposure to terminology.

Compare the prerequisites for advanced electives as well as their availability online. An appealing elective has little decision value if it is not offered during your enrollment window. If your goal is explicitly AI rather than a broader data science education, review affordable online artificial intelligence master's options against the actual elective paths at these two universities.

Neither school's completion time should be reduced to a single advertised number without specifying course load and start term. Ask each program for the earliest and a realistic part-time completion date under its current course rotation. Confirm whether required courses have prerequisites, whether every course runs each term, and whether there is a maximum completion period.

For a working student, the decisive question is often not "Which can I finish fastest?" but "Which sequence can I finish without repeatedly interrupting my job?" A shorter advertised timeline is less useful if it assumes a course load or fixed live meeting times you cannot sustain.

Do not assume that the two programs require the same prior coursework, test scores, essays, or professional experience. Obtain each program's current admissions checklist for the online degree - not a checklist for an on-campus degree with a similar name. Check its stated expectations for mathematics and programming, document requirements, application deadlines, and whether any preparation can be completed after admission.

Applicants changing fields should distinguish an admissions requirement from a skill needed to succeed in graduate coursework. If a program does not formally require a particular course, you may still benefit from strengthening that skill before classes begin. Conversely, self-study does not automatically satisfy a formal prerequisite. If avoiding an essay is a major constraint, machine learning master's options with no admissions statement provide alternatives; verify the current checklist before relying on any no-essay policy.

"Online" does not establish whether attendance is asynchronous, whether group meetings occur at set times, or whether exams require scheduled supervision. Those operational details should determine fit more than a general promise of flexibility. Ask both programs for a sample course calendar and the policy on required live participation, time zones, recorded sessions, and breaks between terms.

Compare those answers against your actual workweek. A student with variable shifts may need recordings and flexible deadlines; someone who learns best through scheduled discussion may prefer regular live contact. Also confirm whether any campus visit is required before budgeting for a fully remote degree.

Boston University and Syracuse University are universities with institutional accreditation, but applicants should verify each institution's current status in the relevant accreditor's directory before enrolling. Institutional accreditation applies to the university; it should not be described as a special accreditation of either data science program unless a separate programmatic accreditation is specifically documented.

If accreditation matters for employer reimbursement, transfer, or future study, ask the organization making that decision which form of recognition it accepts. Also confirm that the online program can enroll a student living in your state; institutional accreditation and state enrollment authorization answer different questions.

Either degree may support applications for data scientist, analytics, or machine-learning-related roles, depending on the student's prior experience and the skills demonstrated through coursework and projects. Neither title alone qualifies someone for a particular job. Compare recent target-role postings with each program's required courses: a role emphasizing production systems calls for different evidence than one emphasizing statistical analysis and communication.

Domain experience can also shape the choice. A professional interested in clinical or health-system problems should look for relevant datasets, project supervision, and privacy-aware methods rather than assume a general data science degree supplies healthcare expertise. Graduate AI pathways into healthcare analytics offer a useful comparison if that specialized direction is your priority.

The BLS reports a $120,230 median annual wage for U.S. data scientists in May 2025. That is the midpoint for people employed in the occupation, not a starting salary or a reported outcome for Boston University or Syracuse graduates. Pay for an individual applicant can differ substantially with experience, employer, role, and location.

BLS projects data scientist employment to grow 35% from 2025 to 2035. Growth indicates occupational demand, not that either degree guarantees a position. For a personal return-on-investment estimate, compare your likely incremental earnings with your net education cost and time commitment; do not compare total projected salary with tuition as though the degree caused all future earnings. Data science degree salary differences across fields can help you test how sensitive your estimate is to your intended industry.

Choose Boston University if its confirmed required courses, project opportunities, schedule, and net price better match your target role and workweek. Choose Syracuse on the same basis - not because "applied" automatically means more hands-on instruction. Without comparable current price quotes and course plans, declaring either a universal winner would be misleading.

Before paying an enrollment deposit, request a degree audit or sample plan from each school for your intended pace. Mark the courses that build the skills missing from your résumé, identify the projects you could discuss in interviews, and compare written total-cost estimates. Treat an unavailable required course, an unworkable live-session schedule, or unexplained mandatory fees as a reason to ask more questions - not a detail to resolve after enrolling.

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• Partnering with Students to Develop a Capstone for a... - PMC https://pmc.ncbi.nlm.nih.gov/articles/PMC11732519/
• MSDS Tuition, Scholarship and Financial Aid | Boston University https://www.bu.edu/cds-faculty/programs-admissions/ms-data-science/msds-tuition-scholarship-and-financial-aid/
• Synchronous vs. Asynchronous Classes | Hopkins EP https://ep.jhu.edu/news/asynchronous-vs-synchronous-classes/
• Syracuse Online Master's in Applied Data Science https://onlinegrad.syracuse.edu/information-science/applied-data-science/
• Proctored Experience Information - eCore https://ecore.usg.edu/current-students/proctored-exams-information/
• Applied Data Science Master's Degree | Syracuse University https://ischool.syracuse.edu/academics/applied-data-science-masters-degree/
• 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/
• Data Science Capstone Experience - Purdue Online https://www.purdue.edu/online/data-science/data-science-capstone-experience/