PhD in Data Science
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
Close faculty interaction, cutting-edge equipment, and personal attention let you structure your program so it suits your individual career goals. You’ll leave with a degree that will help you succeed in your distinctive path.
Data science research gives you opportunities to work on grand challenge problems with societal importance, including topics such as cybersecurity, healthcare, and sustainability.
Our data science graduate program offers expertise in computer science, statistics, and business topics while giving you essential opportunities to work with industry partners.
WPI’s innovative and multidisciplinary graduate program prepares students to become talented and effective leaders in this rapidly evolving field.
Faculty Profiles
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.
Professor Kong’s research interests focus on data mining and machine learning, with emphasis on addressing the data science problems in biomedical and social applications. Data today involves an increasing number of data types that need to be handled differently from conventional data records, and an increasing number of data sources that need to be fused together. Dr. Kong is particularly interested in designing algorithms to tame data variety issues in various research fields, such as biomedical research, social computing, neuroscience, and business intelligence.
Yanhua Li is an Associate Professor in the Computer Science Department and Data Science Program at Worcester Polytechnic Institute (WPI). His research interests focus on artificial intelligence (AI) and data science, with applications in smart cities in many contexts, including spatial-temporal data analytics, urban planning and optimization.
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.
I am Associate Professor of Operations and Industrial Engineering at Worcester Polytechnic Institute (WPI), with courtesy professorships in Mathematical Sciences and Data Science. I hold a Ph.D. in Industrial Engineering from the University of Pittsburgh. My objective is to use science and technology to assist real human need by improving systems that serve vulnerable peoples, such as refugees and asylum seekers, survivors of human trafficking, and children in the foster care system.
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.