Data Science, M.S.

Coordinator: Behrooz Mansouri

Professor: El-Taha; Associate Professor: Greenfield; Assistant Professors: Mansouri, Quinlan, Song, Zhang; Adjunct Faculty: Houser, Levine; Professors Emeriti: MacLeod, Welty

The Master of Science in Data Science is a multidisciplinary program that combines core coursework in computer science and statistics with optional concentrations in applied domains. The program is designed for recent graduates and working professionals with interests in computing, data analytics, and application of data-driven methods in industry or research. Students from diverse academic and professional backgrounds are encouraged to apply, reflecting the program’s commitment to preparing graduates for a wide range of data-focused careers.

Program Requirements

The 30-credit program includes a core set of five required courses (15 credits) that develop technical skills in computer science, mathematics, and statistics, along with three content-area courses (9 credits) within a concentration track, including Business Analytics, Computation, Geographic Information Systems (GIS), Predictive Analytics, Prescriptive Analytics, or Public Health.

Students also must complete a capstone project, either a practicum (3 credits) or a thesis (6 credits). Upon successful completion of the program, graduates will be able to:

Apply reproducible and automated methods to acquire, clean, integrate, and manage diverse data sources, including structured, unstructured, streaming, and multimodal data.
Apply statistical, machine learning, and deep learning methods to analyze large and complex datasets and evaluate model performance using appropriate validation and uncertainty measures.
Design effective data visualizations and communication strategies that translate technical results into actionable insights for diverse stakeholders.
Integrate domain knowledge with data science methods to design, deploy, and maintain scalable, cloud-enabled, and production-ready solutions.
Evaluate trade-offs among accuracy, interpretability, scalability, sustainability, and resource constraints when implementing data-driven systems.
Analyze ethical, legal, and societal implications of data-driven methods, including bias, fairness, transparency, accountability, and responsible AI practices.
Demonstrate effective teamwork, leadership, and professional engagement in interdisciplinary environments and the broader data science community.

Required Courses (15 credits)

Students must complete the following five courses:

Concentration Track (9 credits)

Students must select from one of the following concentration tracks and complete three courses from within that track.

Computing (choose 3)

Predictive Analysis (choose 3)

Geographic Information Systems (choose 3)

Public Health (choose 3)

Cybersecurity (choose 3)

Capstone (6 credits)

Students must complete either a thesis (DSC 698, 6 credits) under the supervision of their concentration track advisors, or a practicum/project (DSC 697, 3 credits) plus one additional data science course.

Ethics

Students must complete ethics training prior to graduation.