One of the first programs of its kind, in the nation, WPI’s interdisciplinary PhD program in Data Science recognizes that traditional data processing applications can no longer handle today’s large and complex datasets. New models are needed to handle big data; and knowledgeable graduates with expertise in turning those observations into meaningful recommendations are in high demand.

You’ll be working alongside faculty and industry partners to analyze, capture, search, share, store, transfer, query, and visualize huge amounts of data to solve real-world challenges. Some broad-stroke examples:

  • using predictive analytics to identify cyber threats
  • employing big data analytics to improve healthcare outcomes
  • empowering “smart” cities to make data-driven policy changes critical for societal well-being

Applying to the Data Science PhD Program

Students will find WPI’s data science degree options listed with engineering, science, and mathematics on the application form.



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.

  1. Core coursework requirements in the 5 categories as detailed below – 21 credits / 7 courses
  2. Electives in coursework – 9 credits / 3 courses
  3. Research credits – 30 credits

TOTAL 60 credits (beyond MS program)


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


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)


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.


IBM Cognos
IBM SPSS Modeler
InfoSphere Big Insights
InfoSphere Streams

Oracle Server
Palisade DecisionTools Suite
SQL Server

Faculty Profiles

Faculty Profiles

Diane M Strong
Professor & Interim Dept Head
Foisie Business School

WPI provides an environment that values both teaching and research, which is ideal for me. I enjoy teaching at WPI because students are interested in learning and willing to work hard. My teaching focuses on how business, healthcare, and nonprofit organizations can best use computing technologies, such as database systems, electronic health records systems, and mobile apps. Students in my classes learn to design computing applications that meet the needs of organizations.

Elke Rundensteiner
Computer Science

As founding Director of the interdisciplinary Data Science program here at WPI, I take great pleasure in doing all in my power to support the Data Science community in all its facets from research collaborations, new educational initiatives to our innovative Graduate Qualifying projects at the graduate level.

Lane Taylor Harrison
Assistant Professor
Computer Science

Information visualization is a powerful means for understanding data and informing human minds. As people begin to rely on visualizations to make high-impact and even life-critical decisions, there is a growing need to ensure that information can be perceived accurately and precisely.

Randy Paffenroth
Associate Professor
Mathematical Sciences

My research focuses on compressed sensing, machine learning, signal processing, and the interaction between mathematics, computer science and software engineering. My interests range from theoretical results to algorithms for tackling practical applied problems, and I enjoy problems most when mathematical results lead to efficient software implementations for big data. I am looking forward to working with students at all levels and backgrounds who share an interest in mathematics, software, or data.

Getting Involved

Getting Involved

We’re data scientists – we use tools of the trade and big data analytics to innovate. Follow department happenings and industry trends via our social media channels on Facebook and LinkedIn.

Students walking around the fountain in the springtime

After Graduation