Jul 25, 2024  
Graduate Record 2021-2022 
Graduate Record 2021-2022 [ARCHIVED RECORD]

Master of Science in Data Science

Return to: School of Data Science: Programs & Courses  

The Master of Science in Data Science (MSDS) is a professional master’s degree program. The residential MSDS program begins with the second summer session, continues into the Fall term, and concludes at the end of the following Spring term (11 months in total). The online MSDS program may be started in the Fall, Spring, or Summer, and can be completed in five semesters. 

Core program courses are taught by faculty from the departments of Computer Science, Statistics, and Systems and Information Engineering, or specifically identified by the School of Data Science. The program utilizes the spiral learning framework, first giving the students the base that they need in languages, computation, and linear modeling, and building upon those skills to move to the practice and application of data science. The program integrates a capstone project where the students use their knowledge and skills to work with an external sponsor to solve a real-world data science problem and present the results in a public presentation.

Course Requirements for the MSDS program:

Course Requirements for the 11-month residential MSDS program are listed below. Course availability and sequencing for the online MSDS program is determined by the School of Data Science.

Summer Term

(Summer Sessions II & III)

Fall Term

Spring Term


Selection of elective courses is done in consultation with the program director. There are a variety of possible electives available, including (but not limited) to those suggested in the Graduate Record. Students are required to take 6 credits of elective credit. In the established residential curriculum, these 6 credits fall within the spring term; other formats of the curriculum may have different sequencing. Elective courses must be at the 5000-level or higher to count for elective credit in the program unless further approval is obtained.