Faculty

View the Data Science Faculty  

The Data Science Program

The Department of Data Science at William & Mary was established in May 2025. Currently, the Applied Science program  offers a Doctor of Philosophy degree with a concentration in Data Science. The Department provides a strong research environment, with faculty actively engaged in areas including machine learning and artificial intelligence, data ethics and trustworthy AI, geospatial analytics, scientific computing, natural language processing, data engineering, network analysis, modeling and simulation.

Course Descriptions

Programs

• Data and Computer Sciences Graduate Certificate

Courses

Course Descriptions

Wherever a William & Mary course is specified as a prerequisite, it is understood that appropriate experience or an equivalent course, taken at another institution, may be substituted for the specified prerequisite. Each such substitution must be approved by the instructor of the course for which a substitution is appropriate. Generally, graduate students should also consult with their advisors to verify that they meet all course prerequisites.

Unless otherwise noted, all courses are graded using standard grading [A, B, C, D, F] scheme (See CDSP: Grading and Academic Progress  in the section entitled Graduate Regulations and may not be repeated for credit (See Repeated Courses  requirements in the section entitled Graduate Regulations).

• DATA 510 - Advanced Applied Machine Learning
• DATA 542 - Neural Networks & Deep Learning
• DATA 543 - Capstone
• DATA 601 - Applied Machine Learning
• DATA 606 - Scientific Writing in Data Science I
• DATA 607 - Scientific Writing in Data Science II
• DATA 621 - Neural Networks & Deep Learning
• DATA 622 - Generative AI
• DATA 623 - Web Science
• DATA 641 - Network Analysis
• DATA 643 - Reinforcement Learning
• DATA 644 - Bayesian Reasoning in Data Science
• DATA 670 - Photogrammetric & Spatial Data in Parallel Environments
• DATA 690 - Readings in Data Science
• DATA 691 - Topics in Data Science