The core curriculum for computational science and engineering and data science draws from courses in computer science, applied math and statistics. Students learn the tools for parallel programming, stochastic optimization and numerical modeling. Elective topics range from machine learning and natural language processing to computational economics or computational biology. Project-based courses provide students with practical experience in collaborative problem solving.

Many students take electives in Applied Computation (AC), a category of courses developed by IACS which apply the methods of computational science and engineering to a domain or class of problems:


Computer Science 109a (AC 209a)
Data Science 1: Introduction to Data Science


Applied Math 207 - Advanced Scientific Computing: Stochastic Methods for Data Analysis, Inference and Optimization


Computer Science 109b (AC 209b)
Data Science 2: Advanced Topics in Data Science


Applied Computation 221
Critical Thinking and Data Science