Mathematics Prerequisites for Data Science Degrees

Data science sits on three branches of mathematics: calculus, linear algebra, and probability and statistics. Almost every master's program expects some of each, but the amount varies widely, from one calculus course to three semesters plus linear algebra. Programs also differ in whether these are hard requirements, recommendations, or topics you can make up after admission. This guide shows how programs phrase their math expectations, using their own published wording, so you can compare your transcript against real requirements rather than guesses. For the full list of materials, see the guide to master's admission requirements.

In our directory (October 2026), 69 of the 102 master's programs we list publish prerequisites of some kind on the pages we reviewed (methodology). The rest may still expect math preparation; they just don't spell it out.

Calculus: from one course to three

Calculus is the most commonly listed requirement, and programs specify it in different ways.

• One course. Notre Dame's online master's says candidates "should have taken at least one university-level calculus course" covering differentiation and integration, with a grade of C or higher. Rochester's MS asks for "undergraduate mathematics experience through basic calculus."
• Two semesters. DePaul requires "Completion of Calculus I and Calculus II with at least a B- average," with exceptions for extensive analytics work experience. Michigan's Data Science Master's Program in Statistics expects "2 semesters of college calculus," and Texas A&M's online Statistical Data Science M.S. asks for "one semester each of Calculus 1 and Calculus 2."
• Through multivariable calculus. SMU's M.S. in Data Science and Applied Statistics requires calculus "through multivariate calculus." The University of Arizona's Statistics & Data Science program expects "at least three semesters of Calculus" through multivariable or vector calculus.

Statistics-housed programs tend to sit at the upper end. If a program lives in a statistics department, expect multivariable calculus.

Linear algebra

Linear algebra is the language of most machine learning methods, and many programs list it separately:

• The University of Wisconsin-Madison's Data Science MS lists MATH 221 and MATH 222 (calculus) plus MATH 340 or MATH 345 (linear algebra) as courses applicants should have completed or matched.
• Michigan's program expects "1 semester of linear or matrix algebra."
• Texas A&M's online program asks for "a knowledge of matrix algebra" rather than a named course.
• CU Boulder's residential M.S. says applicants should be familiar with differential and integral calculus, linear algebra, and "have some experience with infinite sequences and series."

When a program names course numbers, use its catalog descriptions to check whether your own course covered the same topics. A "business math" course with a matrix chapter is not usually the same thing as a semester of linear algebra.

Probability and statistics

Statistics requirements vary from a single introductory course to calculus-based probability:

• American University's online MS in Data Science lists an introductory statistics course (such as STAT-202, STAT-203 or STAT-204) and notes it "may be waived" for applicants with comparable education or experience.
• The University of Texas at Austin's online M.S. in Data Science asks applicants to complete calculus and linear algebra equivalent to MATH 408D and MATH 341, and statistics equivalent to SDS 320E, before the semester they intend to enroll.
• Brown's Sc.M. recommends one semester of calculus-based probability and statistics, alongside a semester each of calculus and linear algebra.

Rochester is a useful contrast: its MS says students "do not need college-level statistics or data analytics" before starting, though calculus and some programming are expected.

Required, recommended or "no fixed prerequisites"

The same topics can carry very different weight depending on the wording.

• Required before you start. UVA's residential M.S. lists "four prerequisites": Calculus II, Linear Algebra, Programming and Statistics. You can apply with some missing, but they must be completed before matriculation.
• Expected, not formally required. Harvard SEAS says there are no formal prerequisites, yet successful applicants need a "comfortable working knowledge of calculus, linear algebra and differential equations" and familiarity with probability and statistical inference. Brown similarly says "There are no fixed prerequisites for the program," then lists a recommended minimum.
• Described as a level, not a list. NYU's MS in Data Science requires "substantial but specific mathematical competencies," typical of a major in mathematics, statistics, engineering, physics, theoretical economics or computer science.

"No formal prerequisites" does not mean no math. It usually means the committee will judge your preparation from your whole file instead of checking boxes.

What counts as proof

Programs accept evidence in different forms, and a few rule some forms out. Notre Dame states that its admissions committee "does not consider certificates and MOOCs to be a university-level course" for the calculus requirement. Harvard, on the other hand, says that if your training is not reflected in your transcript courses, you need to make it clear elsewhere in the application.

Before relying on a non-credit course, a professional certificate or work experience, check the program's exact wording. DePaul's analytics-experience exception and American University's waiver language are examples of programs that say alternatives exist; many others do not.

Filling gaps before you apply

If your transcript is short on math, programs themselves point to several routes:

• Program-run refreshers and bootcamps. UVA offers free, asynchronous bootcamps in calculus, linear algebra and programming to admitted students after the program deposit. Notre Dame offers a Calculus Refresher, and American University's online program requires a mathematical boot camp before the program starts.
• For-credit courses. A community college or university course gives you a transcript line, which is the form most programs accept without question. Notre Dame explicitly suggests enrolling in a university-level calculus course.
• Course-number matching. Where a program lists course numbers, as Wisconsin-Madison and UT Austin do, choose a course whose description covers the same topics.

Plan your timeline around the deadline that matters: some programs want prerequisites done when you apply, others before your first term. If programming is also a gap, see the guide to programming prerequisites.