WPI’s Data Science PhD program is interdisciplinary, drawing from Computer Science, Mathematical Sciences, and the Foisie Business School. Together, courses and dissertation research revolve around five key areas:
- Integrative Data Science
- Business Intelligence and Case Studies
- Data Access and Management
- Data Analytics and Mining
- Mathematical Analytics
Plan of Study
PhD requirements include coursework as well as a research component. Together they total a minimum of 60 credit hours beyond the Data Science master’s degree requirement. Students entering the Ph.D. Program with a bachelor’s degree first complete the M.S. in data science at WPI using the M.S. Thesis option as first step towards their Ph.D. degree.
Each Ph.D. student is assigned an Academic Advisor and together they formulate a Plan of Study that then is approved by the Data Science Steering Committee.
- Core coursework requirements in the 5 categories as detailed below – 21 credits / 7 courses
- Electives in coursework – 9 credits / 3 courses
- Research credits – 30 credits
TOTAL 60 credits (beyond MS program)
CORE DATA SCIENCE COURSEWORK REQUIREMENT (21 CREDITS / 7 COURSES)
A Ph.D. student must obtain core competency by taking 7 courses from the below list of Data Science core areas, with an A grade in 4 out of the 7 courses and at least a grade B for the remaining 3 courses, within 2 years after starting the Ph.D. 60 program.
Integrative Data Science (Required)
DS 501. Introduction to Data Science (3 credits)
Mathematical Analytics 3 credits (Select at least one)
DS 502. Statistical Methods for Data Science (3 credits)
MA 542. Regression Analysis
MA 554. Applied Multivariate Analysis
Data Access and Management 3 credits (Select at least one)
CS 542. Database Management Systems (3 credits)
MIS571. Database Applications Development
DS 503. Big Data Management (3 credits)
CS 561. Advanced Topics in Database Systems
Data Analytics and Mining 3 credits (Select at least one)
CS 548. Knowledge Discovery and Data Mining (3 credits)
DS 504. Big Data Analytics (3 credits)
CS 539. Machine Learning
Business Intelligence and Case Studies 3 credits (Select at least one)
MIS 584. Business Intelligence
MKT 568. Data Mining Business Applications
ELECTIVES
Nine more course credits must be taken, with the listing of courses pre-approved as electives for the Data Science program found in the WPI Graduate Catalog under Data Science. Other graduate courses, graduate research credits, or ISGs may also be used, with prior approval of the Data Science Steering Committee.
Two elective courses designed as ramp up courses for students who may be lacking in sufficient background in either statistics or programming, respectively, are listed below. They can count towards the 33 credits of the DS MS degree, However, they cannot be used to meet the above requirements in five core categories.
MA 511. Applied Statistics for Engineers and Scientists
CS 5007. Intro to Applications of CS with Data Structures and Algorithms (Programming for non-CS majors)
RESEARCH CREDITS (30 credits)
At least 30 credits must be research credits, consisting of DS 597 Directed Research and DS 699 Dissertation Research. Prior to Admission to Candidacy, a student may receive up to 18 credits of Pre-Dissertation Research under DS 597. Only after Admission to Candidacy by passing the Research Qualifier may a student receive credit toward Dissertation Research under DS 699.
As part of the research component, PhD students pass a Qualifying Examination and propose and defend Dissertation Research. Learn more about the Ph.D. milestones, including the Ph.D. Qualifying Examination, the Ph.D. dissertation proposal and Ph.D. final dissertation defense.
