MS Degree Requirements

Students applying to the M.S. Degree program in Data Science (DS) are expected to have a bachelor's degree with a strong quantitative and computational background including coursework in programming, data structures, algorithms, univariate and multivariate calculus, linear algebra and introductory statistics. Students with bachelor's degrees in computer science, mathematics, business, engineering and quantitative sciences would typically qualify.

Requirements for the M.S. Degree

Students pursuing the M.S. degree in Data Science must complete a minimum of 33 credits of relevant work at the graduate level. These 33 credits must include either the 3-credit Graduate Qualifying Project (GQP) requirement or the 9-credit M.S. thesis, and the core coursework requirements in Data Science as described below. These M.S. degree requirements have been designed to provide a comprehensive yet flexible program to students who are pursuing an M.S. degree exclusively and also students who are pursuing a combined B.S./M.S. degree.


Upon acceptance to the M.S. program, students will be assigned an academic advisor. In consultation with the academic advisor, the student must prepare a Plan of Study outlining the selections that the student will make to satisfy the M.S. degree requirements among the options offered. This Plan of Study must then be approved by the Data Science Program Review Board.

  1. Core Data Science Coursework Requirement (15 credits)
    A student in the M.S. program must take Data Science course work to satisfy the Data Science core coursework requirements, namely, they must take all courses in the Integrative Data Science category (currently one) and one [1] course from each of the other core Data Science categories listed below:
    1. Integrative Data Science (Must take all):
      1. * DS 501 Introduction to Data Science
    2. Mathematical Analytics (Select one):
      1. *MA 543/DS 502 Statistical Methods for Data Science
      2. MA 542. Regression Analysis
      3. MA 554. Applied Multivariate Analysis
    3. Data Access and Management (Select one):
      1. *CS 542. Database Management Systems
      2. *MIS 571. Database Applications Development
      3. CS 561. Advanced Topics in Database Systems
      4. CS 585/DS 503. Big Data Management
    4. Data Analytics and Mining (Select one):
      1. *CS 548. Knowledge Discovery and Data Mining
      2. CS 539. Machine Learning
      3. CS 586/DS 504. Big Data Analytics
    5. Business Intelligence and Case Studies (Select one):
      1. *MIS 584. Business Intelligence
      2. MKT 568. Data Mining Business Applications

    [1] Additional courses may be added into the Integrative Data Science category and the other categories in the future.

    If a student does not have prior background in a particular core category, then the student is advised to take the course with a star (“*”) within that category. If two or more courses are starred, then the student may select either of these starred courses based on their individual interest and background. Students must take at least 1 course in each of these core areas, but are encouraged to take several. Additional courses taken in a core category will count as electives and/or concentration courses as described in part III below.

  2. Graduate Qualifying Project / M.S. Thesis / (3-9 credits)

    A student in the M.S. program must complete one of the following two options:
    • A 3-credit Graduate Qualifying Project. (DS 598) This project is most commonly done in teams, and will provide a capstone experience in applying data science skills to a real-world problem. It will be carried out in cooperation with a sponsor or an industrial partner, and must be approved and overseen by a faculty member affiliated with the Data Science Program. A student that follows this practice-oriented project option must gain sufficient Data Science depth by selecting at least 2 courses beyond the required Data Science core courses from among the electives below within the same area of concentration.
    • A 9-credit Master’s Thesis. (DS 599) A thesis in the Data Science Program consists of a research or development project worth (a minimum of) 9 graduate credit hours. Students interested in research, and in particular those who are considering to pursue a Ph.D. degree in a related area, are encouraged to select the M.S. thesis option. Any affiliated DS faculty may serve as the thesis advisor. If the advisor is not a tenure-track faculty at WPI, then a DS affiliated tenure-track faculty must serve as the thesis co-advisor. First, a thesis proposal must be approved by the DS Program Review Board and the student’s advisor before the student can register for more than three thesis credits. The student then must satisfactorily complete a written thesis and present the results to the DS faculty in a public presentation.
  3. Electives and Areas of Concentration (9-15 credits)

    A student in the M.S. in Data Science program must take course work from the Program electives to satisfy the remainder of the 33 credit requirement. An elective may be any of these graduate-level courses, with the restriction that no more than 16 credits of the 33-credit Data Science degree program may be courses offered by the School of Business.

    While the core areas ensure that students have adequate coverage of essential Data Science knowledge and skills, the wide variety of electives allows students to tailor their Data Science degree program to domain and technique areas of personal interest. Students are expected to select electives to produce a consistent program of study. While the core coursework requirements provide the needed breadth in Data Science core categories, students can gain depth in one or several concentrations by choosing appropriate electives from the list of pre-approved courses relevant to data science.

    Other courses beyond the pre-approved Program electives may also be chosen as electives, but only with prior approval by the DS Program Review Board, and if consistent with the student’s Plan of Study. For example, students might choose to concentrate their data science expertise on areas of physics, engineering or sciences not captured in the electives. Independent study and directed research courses also require prior approval by the DS Program Review Board.