M.S. in Data Science

Program of Study
Degree Type
Master of Science

Students pursuing the M.S. degree in Data Science must complete a minimum of 30 credits of relevant work at the graduate level. These 30 credits must include the core coursework requirements in Data Science (see below) and either a 3-credit Graduate Qualifying Project (GQP) or a 9-credit M.S. thesis. 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 who will work with the student to correctly prepare a Plan of Study. This Plan of Study must then be approved by the Data Science Program Review Board.

GQP Track

Graduate Qualifying Project

Minimum Credits

Required Core Areas

Minimum Credits

Concentration and Electives

Minimum Credits

M.S. Thesis Track

Minimum Credits
Minimum Credits

Concentration and Electives

Minimum Credits

Core Data Science Coursework Requirement (15 credits)

Students in the M.S. program must take both courses in the Integrative Data Science category and one (1) course from each of the other core Data Science categories listed below:

Integrative Data Science (required):

Minimum Credits

Business Intelligence and Case Studies (Select one):

Minimum Credits

If a student has completed a B.S. degree in Data Science at WPI, then the “Integrative Data Science” core area requirement is waived. Instead, the student can earn the corresponding 3 credits by taking any of the data science courses listed in the graduate catalog, including DS 501.

If a student does not have prior background in a particular core category, then it is advised that the student take the course with an asterisk * in the title within that category. If two or more courses have an asterisk *, then the student may select either of these courses based on their personal 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 below.

Graduate Qualifying Project GQP or M.S. Thesis

A student in the M.S. program must complete one of the following two options:

  • 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. The graduate qualifying project is typically taken for 3 graduate credits. With permission by the instructor, a student can take the course a second time for additional credit, up to a total of 6 graduate credits. This means that the student could take two offerings of the course concurrently in one semester or could register for three credits in one semester and another three credits in a subsequent semester. 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.
  • 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 a Ph.D. 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 member must serve as the thesis co-advisor. A thesis proposal must be approved by both the DS Program Review Board and the student’s advisor before the student can register for more than three thesis credits. The student must then satisfactorily complete a written thesis and present the results to the DS faculty in a public presentation.

Electives and Areas of Concentration (6-12 credits)

A student seeking an M.S. in Data Science program must take course work from the Program electives listed below in order to satisfy the remainder of the 30 credit requirement. An elective may be any of these graduate-level courses, with the restriction that no more than 14 credits of the 30-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 enable students to tailor their Data Science degree program to domain and technique areas of personal interest. Students are expected to select elective course work to produce a consistent program of study. While the core coursework requirements provide the needed breadth in Data Science core categories, students will 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 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 below. Independent study and directed research courses also require prior approval by the DS Program Review Board.

List of Program Elective Courses:

Relevant Business Graduate Courses (a maximum of 14 graduate credits of School of Business coursework may count toward the M.S. in Data Science):
    BUS 500. Business Law, Ethics and Social Responsibility
    FIN 500. Financial Management
    FIN 503. Financial Decision Making for Value Creation  
    MIS 500. Innovating with Information Systems
    MIS 571. Database Applications Development
    MIS 573. Systems Design and Development
    MIS 576. Project Management
    MIS 581. Policy and Strategy for Information Technology and Analytics
    MIS 583. User Experience Applications
    MIS 584. Business Intelligence  
    MIS 585. User Experience Design
    MIS 587. Business Applications in Machine Learning
    MKT 568. Data Mining Business Applications
    OBC 505. Teaming and Organizing for Innovation  
    OBC 506. Leadership
    OIE 501. Operations Management
    OIE 542. Risk Management and Decision Analysis
    OIE 544. Supply Chain Analysis and Design
    OIE 552. Modeling and Optimizing Processes
    OIE 559. Advanced Prescriptive Analytics: From Data to Impact
Relevant Computer Science Graduate Courses:
    CS 5007. Introduction to Applications of Computer Science with Data Structures and Algorithms
    CS 5084. Introduction to Algorithms: Design and Analysis
    CS 504. Analysis of Computations and Systems
    CS 509. Design of Software Systems
    CS 525. Topics in Computer Science (with prior approval of the Program Review Committee to determine relevancy)
    CS 528. Mobile and Ubiquitous Computing
    CS 534. Artificial Intelligence
    CS 536. Programming Language Design
    CS 539. Machine Learning  
    CS 541/DS 541. Deep Learning
    CS 542. Database Management Systems
    CS 545. Digital Image Processing
    CS 546. Human-Computer Interaction
    CS 547/DS 547. Information Retrieval
    CS 548. Knowledge Discovery and Data Mining
    CS 549. Computer Vision
    CS 561. Advanced Topics in Database Systems
    CS 573. Data Visualization
    CS 584. Algorithms: Design and Analysis
    CS 585/DS 503. Big Data Management
    CS 586/DS 504. Big Data Analytics
Note: Students may not receive credit for both CS 5084 and CS 584
Relevant Mathematical Sciences Graduate Courses:
    MA 511. Applied Statistics for Engineers and Scientists
    MA 517/DS 517. Mathematical Foundations for Data Science
    MA 529. Stochastic Processes
    MA 540. Probability and Mathematical Statistics I
    MA 541. Probability and Mathematical Statistics II
    MA 542. Regression Analysis
    MA 543/DS 502. Statistical Methods for Data Science
    MA 546. Design and Analysis of Experiments
    MA 547. Design and Analysis of Observational and Sampling Studies
    MA 549. Analysis of Lifetime Data
    MA 550. Time Series Analysis
    MA 552. Distribution-Free and Robust Statistical Methods
     MA 554. Applied Multivariate Analysis
    MA 556. Applied Bayesian Statistics
Relevant Learning Sciences and Technology Program Graduate Courses:
    CS 565. User Modeling
    CS 566. Graphical Models For Reasoning Under Uncertainty
    CS 567. Empirical Methods For Human-Centered Computing
    PSY 505. Advanced Methods and Analysis for the Learning and Social Sciences
Relevant Bioinformatics and Computational Biology Program Courses:
    BCB 501. Bioinformatics
    BCB 502/CS 582. Biovisualization
    BCB 503/CS 583. Biological and Biomedical Database Mining
    BCB 504/MA 584. Statistical Methods in Genetics and Bioinformatics
Relevant Biomedical Engineering Courses:
    BME 595. Special Topics: Machine Learning for Biomedical Informatics
Relevant Electrical and Computer Engineering Department Courses:
    ECE 502. Analysis of Probabilistic Signals And Systems
    ECE 503. Digital Signal Processing
    ECE 504. Analysis of Deterministic Signals And Systems
    ECE 578/ CS 578. Cryptography and Data Security
    ECE 630. Advanced Topics in Signal Processing
    ECE 673/CS 673. Advanced Cryptography
    ECE 5311. Information Theory and Coding
Other Relevant Graduate Courses and Concentration Areas:
Beyond courses in the three core disciplines of computer science, business, and statistics, relevant graduate courses in other potential areas of concentration, such as Finance, Manufacturing, Healthcare, National Security, Engineering, Fraud Detection, Science, Smart Grid Management, Sustainability and the like, may be added in the future to the above list of pre-approved Program electives.

Specializations of the Data Science Degree:

Specializations of the Data Science degree in targeted areas of high societal impact ranging from Health Care to National Security may be designed in the future. We expect these specializations to naturally fit into the flexible structure of the Data Science degree framework.