Data Science & Artificial Intelligence

Introduction to Data Science & Artificial Intelligence (DSAI)

The DATS Program: A Brief History

The Data Science (DATS) program at Penn Engineering’s School of Engineering and Applied Science officially launched in the fall of 2017, welcoming an inaugural cohort of 25 students. Built on Penn Engineering’s strengths in applied mathematics, machine learning, and algorithms, the program grew rapidly—reaching over 240 students total by 2026—reflecting sustained demand for rigorous, technically grounded training at the intersection of statistics, computation, and real-world data.

As artificial intelligence emerged as a defining force in both research and industry, student and employer demand for deeper AI training grew alongside it. Penn Engineering responded by expanding its AI faculty and course offerings—laying the groundwork for a natural evolution of the program. Rather than launching a separate degree, DATS is expanding into DSAI: a unified program with two concentrations—Data Science and Artificial Intelligence—sharing a strong common core while giving students the flexibility to specialize.

DSAI Program Overview

Penn’s Master of Science in Engineering (MSE) in Data Science & Artificial Intelligence (DSAI) prepares students for a wide range of data-driven and AI-powered careers, whether in technology and engineering, consulting, healthcare, science, policy-making, or understanding patterns in literature, art or communications.  Building on a shared foundation in statistics, optimization, machine learning, and algorithms, students can tailor their studies through two concentrations—Data Science, which includes coursework on data management and decision making, and Artificial Intelligence, which includes coursework on building systems that can perform tasks that require human intelligence.  The DSAI Program can typically be completed in one-and-a-half to two years.

Data Science & AI at Penn

Penn provides the perfect environment for data science and AI enthusiasts, with its strong cross-disciplinary traditions. Biomedical informatics, communications and public policy, robotics, machine learning and artificial intelligence, and data privacy are of broad interest across campus.