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

These programs address the same broad goal, graduate study in data science, but their names do not establish a difference in technical rigor or career outcomes. Use equivalent measures for both schools: required credits, compulsory courses, electives you can actually take online, total cost, and attendance obligations.

The table identifies what the available information establishes and what remains a decision-critical question.

If your target is specifically artificial intelligence rather than a broader data science role, compare each school's AI coursework with dedicated AI degree programs for online students. That is a more meaningful distinction than treating every program with machine learning courses as an AI degree.

A defensible Syracuse-versus-Pace price comparison requires the same starting term and the same definition of cost. A per-credit quote is not a program total, and neither school's current total should be assumed without its applicable tuition schedule and degree requirements.

Request an itemized estimate from each school that separates these amounts:

• Tuition: the rate applicable to you multiplied by the credits you must complete, accounting for any approved transfer credits.
• Mandatory fees: charges required for online enrollment, courses, or graduation.
• Situational expenses: materials, software or equipment, and travel if a visit is required.
• Financing costs: interest and fees if you borrow, offset where applicable by confirmed scholarships or employer assistance.

Ask whether tuition can change while you are enrolled and whether aid applies to the course load you plan to take.

If one school offers a lower sticker price but requires a schedule that reduces your paid working hours, that opportunity cost also belongs in your personal comparison. Do not treat a possible future salary increase as money already available to pay the bill.

The useful curriculum question is not whether one title sounds more practical or technical. It is whether each school's required courses develop the skills you need, and whether the electives that fill your gaps are offered online often enough to fit your timeline. Compare the current catalogs side by side for statistics, programming, data management, machine learning, communication, and a substantial project or capstone.

A dedicated course generally provides stronger evidence of depth than a topic mentioned in a course description. Likewise, a portfolio-ready project depends on access to data, feedback, and an opportunity to explain your methods and results.

A capstone label by itself does not establish those features. Ask both programs for recent online course offerings and a description of the final project requirements.

If responsible AI development is central to your goals, check whether either curriculum offers substantive work on bias, privacy, and governance rather than assuming those topics are covered in a general machine learning class. Dedicated AI governance and ethics graduate study options provide a useful point of comparison.

A published completion time may assume a particular number of courses each term. For your own timeline, the decisive questions are how many credits remain after any approved transfer work, how often required courses run, whether prerequisites impose a sequence, and how many courses you can manage while working.

Ask each advisor to map a term-by-term route from your intended start date through graduation, including what happens if you skip a term. A shorter advertised timeline is less useful if a missed required course delays completion.

If speed is your first priority, compare the resulting schedules with other accelerated online data science master's options rather than assuming either degree is the quickest route.

Do not assume Syracuse and Pace use identical prerequisites or that prior professional experience replaces a formally required course. Before applying, check each program's current admissions page for degree requirements, transcripts, quantitative or programming preparation, recommendations, statements, testing policies, and any deadlines relevant to your start term. Requirements and waivers can vary by applicant and may change.

If you are moving into data science from another field, ask admissions which gaps must be resolved before admission and which skills you can develop before classes begin. Coursework, work projects, and self-study can demonstrate readiness, but only the program can determine whether they satisfy a formal prerequisite. The same distinction matters when considering AI master's degrees for students from other academic fields.

"Online" does not tell you whether attendance is flexible. For both programs, confirm whether classes require live participation, when those sessions occur in your time zone, whether recordings are available, and how group projects and exams are scheduled. Also check for any campus visits or other in-person requirements before you enroll.

If you work unpredictable hours, a required live class can matter more than a modest difference in tuition. If you want frequent real-time discussion, the opposite may be true. Ask to see a sample weekly course schedule from each program rather than relying on the word "flexible."

Check each university's current institutional accreditation through the university and its accreditor. Then distinguish that status from any claim of program-specific accreditation: institutional accreditation does not, by itself, establish a separate accreditation for a data science degree. 

Before paying a deposit, also confirm that the online program can enroll students in your state and whether a planned move would affect your enrollment. Those are separate questions from accreditation and can matter even when coursework is delivered remotely.

Either degree may help you prepare for data scientist, analytics, or other data-focused positions, depending on your prior experience and the skills demonstrated in your coursework and projects. Employers hire for particular tasks, so compare job descriptions you would realistically pursue with each program's required courses, not just its degree title.

For example, a role centered on production machine learning calls for different project evidence than one centered on explaining analyses to business stakeholders. Ask each school what students actually build and whether career services support online learners. Neither degree, on its own, establishes eligibility for a particular job.

BLS reports a $120,230 median annual wage for U.S. data scientists in May 2025. It is an occupation-wide figure, not a starting salary or a reported outcome for Syracuse or Pace graduates. Your pay could differ substantially with your location, experience, employer, and role.

BLS also projects data scientist employment to grow 35% from 2025 to 2035. That outlook indicates demand across the occupation, but it does not measure either program's placement rate or the additional earnings caused by a master's degree. If a school supplies graduate salary figures, ask which graduates responded, their prior experience, and whether the figure represents salary level or salary increase.

Choose the program that closes your most important skill gaps at a cost and pace you can sustain. Syracuse is the better choice for you if its verified required courses and available projects fit your target work more closely and its full cost and schedule are acceptable.

The same test favors Pace if its verified curriculum, scheduling, or net price serves you better. Neither conclusion follows from the names of the degrees alone.

Before deciding, obtain a written cost estimate and a term-by-term course plan from both schools. Compare those documents against a small set of target job descriptions, then confirm any admissions conditions, live attendance requirements, and state enrollment restrictions.

A common mistake is to compare one school's tuition quote with the other school's all-in estimate; another is to count an attractive elective without checking when online students can take it.

If your objective is primarily AI work, use planning an AI degree around professional objectives to test whether either data science curriculum is the right credential before committing to a school.

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• 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
• 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/
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• Capstone projects as opportunities for real-life applications | Trends and Issues in Higher Ed https://www.washington.edu/trends/capstone-projects-as-opportunities-for-real-life-applications/
• Applied Data Science Master's Degree | Syracuse University https://ischool.syracuse.edu/academics/applied-data-science-masters-degree/
• Graduate Admission Requirements | University of Cincinnati Admissions https://www.admissions.uc.edu/apply/graduate/requirements.html