Programming Prerequisites for Data Science Degrees

Programming is the second pillar of data science admissions, next to math. Programs rarely ask for a computer science degree, but most expect you to write code on day one, and many say which languages they have in mind. The level they expect ranges from "basic proficiency in Python" to the equivalent of a computer science minor. This guide sets out how programs describe programming prerequisites, which languages come up, and the bridge or foundation courses some programs publish for applicants who are not there yet.

How much programming programs expect

Published expectations fall into roughly four levels.

• Basic proficiency in one language. Michigan's online Master of Applied Data Science lists "Basic proficiency in Python" among its requirements. Texas A&M's M.S. in Data Science asks for "a beginner's to intermediate understanding of at least one programming language."
• Object-oriented programming. Indiana University's online M.S. asks for "Object-oriented programming knowledge in C/C++, Python and/or R," and Berkeley's online MIDS asks for basic proficiency in object-oriented programming such as Python, Java or C++.
• Programming plus core computer science. Illinois Tech's Master of Applied Science in Data Science lists a high-level language at the level of CS 201 (object-oriented programming required) and a data structures and algorithms course at the level of CS 331, among other prerequisites.
• A computer science background by course count. Penn's online MSE in Data Science accepts applicants with a computer science degree, or a degree plus "at least four for-credit computer science courses," such as introductory programming, discrete math, computer systems and data structures. A third route is a quantitative degree plus two years of quantitative and coding work experience.

At the other end, Northwestern's online MS in Healthcare Data Science says "No prior programming experience is required," with Python and R taught from foundational coursework onward. Programs like this are the exception, and are often built for a specific audience.

Python, R and SQL

Python and R are the two languages programs name most. Some examples from published pages:

• SMU's online M.S. in Data Science asks for a basic understanding of a language "such as SAS, JAVA, C, C++, Python or R," and adds that a basic understanding of Python and R is strongly recommended.
• Texas A&M's list of acceptable languages includes R, MATLAB, Python, C++, SQL and Java.
• CU Boulder's residential M.S. says applicants should have "some programming experience, whether it is formal, informal or on the job," and that some advanced knowledge of R is helpful.
• The University of Wisconsin-Madison's Data Science MS lists one programming foundation course (COMP SCI 220, 300 or 320) and recommends previous coursework or significant experience in R.

SQL is more often taught than required. Syracuse's online M.S. in Applied Data Science, for instance, says students gain proficiency in Python, R and SQL during the curriculum. If a program lists SQL as a prerequisite, treat it as one; if it does not, basic SQL is still useful preparation because database work runs through most curricula.

If you know one general-purpose language well, most programs that name "Python or R" will accept that as a starting point. Programs that specify object-oriented programming, data structures or algorithms are asking for more than scripting.

How programs want you to show it

Programming skill does not always appear on a transcript, and programs handle that differently.

• Coursework or experience. Berkeley says proficiency "may be demonstrated through academic coursework or professional experience."
• Documented in the résumé. The University of North Texas asks for a résumé or CV showing knowledge of programming languages ("at least one language") and an experience write-up explaining how your coursework, jobs or training meet its requirements.
• Placement or proficiency exams. Illinois Tech notes that "Proficiency and placement exams are also available," and Berkeley offers a waiver exam to place out of its introductory programming course.

Whatever form a program accepts, be specific. Name the languages, the kind of work (analysis scripts, production code, data pipelines) and roughly how long you used them.

Bridge and foundation courses programs publish

Several programs build a path for applicants with gaps instead of turning them away. Read the terms carefully: some bridge courses count toward the degree, some do not, and some must be finished before you enroll.

• Required first-term course. Berkeley's online MIDS says applicants with limited programming experience must take DATASCI 200, Introduction to Data Science Programming, in their first term.
• Conditional admission with bridge courses. Syracuse's online program says applicants without a coding or math foundation "may be admitted conditionally and required to complete bridge courses."
• Foundation courses or modules. Kennesaw State's M.S. in Data Science and Analytics says required background can be shown through transcript coursework, "by completing foundation courses, or by completing foundation modules."
• Free introductory courses. Clarkson says free introductory courses are offered online for accepted candidates whose undergraduate background did not cover calculus, programming and statistics.
• Summer bridging. Rochester's MS requires a summer bridging course for admitted students who lack data structures.
• Prerequisite courses outside the degree. Illinois Tech requires students with insufficient background to take prerequisite courses with at least a B, and states these "do not count toward the 33 credit hour requirement." Boston University says applicants short on one or two prerequisites "may be admitted with prerequisite courses."

Extra courses can add time and, where they carry tuition, cost. Ask whether a bridge course is billed, whether it counts toward graduation, and whether financial aid covers it.

Preparing before you apply

• Pick one language and go deep. Python or R is a safe choice for most programs; check whether yours names one.
• Cover the basics programs list. Data types, control flow, functions, reading and cleaning data files, and simple plots. For programs that name them, add object-oriented programming, data structures and algorithms.
• Get evidence on paper. A for-credit course gives you a transcript line. If you learned on the job, describe projects concretely in your résumé and statement.
• Check timing. Some programs want proof at application, others before the first term.

Math expectations often come bundled with programming ones; see math prerequisites. If you are coming from a non-technical field, the guide to applying without a computer science degree covers which requirements matter most. You can also browse online master's programs and filter by what each one publishes.