Courses

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:

CS109a and CS109b: Data Science 1 and 2

These two courses are the core of an introduction to data science at Harvard. CS109a focuses on the analysis of data to perform predictions using statistical and machine learning methods. Topics include data scraping, data management, data visualization, regression and classification methods, and deep neural networks. CS109b addresses advanced topics in data science and includes modules with real world data and applications.

Click here to view the CS109 website.

Capstone Course

Developed by IACS Scientific Program Director, Pavlos Protopapas, the Capstone Research course is a group-based research experience where students work directly with a partner from industry, government, academia, or an NGO to solve a real-world data science/ computation problem. Students will create a solution in the form of a software package, which will require varying levels of research. Upon completion of this challenging project, students will be better equipped to conduct research and enter the professional world. 

Link to Capstone Course.