One of the first programs of its kind, in the nation, WPI’s interdisciplinary PhD 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 applying to the data science PhD program will find WPI’s data science degree options listed with engineering, science, and mathematics on the application form.
Curriculum for PhD in Data Science
WPI’s PhD in data science is interdisciplinary, drawing from Computer Science, Mathematical Sciences, and the 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
Fostering the entrepreneurial mindset in STEM
Chemical Engineering Professor David DiBiasio and colleagues at WPI with sponsorship from the KERN Foundation are leading efforts to introduce entrepreneurial training into WPI’s STEM curricula and project based learning.
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
Apps for Better Healthcare
Diane Strong (left), professor, and Bengisu Tulu, associate professor, are leading teams developing smartphone apps that help people better manage health conditions ranging from diabetes to stress eating. The work draws on the expertise of a team of specialists, including technology experts and clinicians.
Improving Migration Conditions at the Borders
Andrew Trapp, associate professor of operations and industrial engineering at the Foisie Business School, develops analytical tools to estimate capacities for holding sites, judges, and other resources needed to humanely process migrant asylum cases at the international borders.
Research
Faculty Profiles
Getting Involved
WPI is proud to be the recipient of not one, but two National Science Foundation Research Traineeship programs. The programs provide exceptionally talented graduate students with specialized training and funding assistance to join careers at the forefront of technology and innovation. The programs are for graduate students in research-based master's and doctoral degree programs in STEM. Learn more.
The BioPoint Program for Graduate Students has been designed to complement traditional training in bioscience, digital and engineering fields. Students accepted into one of the home BioPoint programs will have the flexibility to select research advisors and take electives in other departments to broaden their skills. BioPoint curriculum is designed to be individual, interactive, project-focused and diverse, and includes innovative courses, seminars, journal clubs and industrial-based projects. Learn more.