Applied Computation master's programs offer a rigorous foundational education with the flexibility for students to pursue their own interests through the following programs:
- Master of Science in Computational Science and Engineering: eight courses, usually over two semesters
- Master of Engineering in Computational Science and Engineering: eight courses, plus one year of research leading to a master’s thesis
- Secondary Field in Computational Science and Engineering: four courses, open to all Harvard Ph.D. students
NEW: Master of Science in Data Science: twelve courses, usually over three semesters, beginning fall 2018
Many graduates go on to work across a variety of industry sectors, including technology, government, financial, marketing, media and consulting. Other graduates choose to pursue further studies at leading graduate business or doctoral programs.
The core curriculum is equally balanced between 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 data science 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:
AC 209a: Data Science 1: Introduction to Data Science
AC 209b: Data Science 2: Advanced Topics in Data Science
AC 221: Critical Thinking and Data Science
AC 227: Computational Physics
AC 290: Extreme Computing
AC 297r: Capstone Project Course
AC 298r: Interdisciplinary Seminar in Computational Science and Engineering
AC 299r: Special Topics in Applied Computation
Students in computational science and engineering work on projects addressing real-world problems in science and society. Industry and government partners, along with Harvard University science and medicine researchers provide a rich source of data, valuable domain expertise, and important problems to solve.
Chile-Harvard Innovative Learning Exchange Program (CHILE)
Students from Harvard University and University of Chile join together in Chile to solve problems drawn from astronomical data collected at various observatories in Chile. Students gain the opportunity to work collaboratively with real world application on problems which cross the disciplines of machine learning, statistics, astronomy, and mathematics.
Data Shack Project- Milan/Cambridge (capstone course project)
Computational science and engineering students work on international teams to solve problems integrating data management, machine learning, data analysis, statistics and mathematics, data visualization and user experience design.
Team Research in Computational and Applied Mathematics (TRiCAM): Undergraduates from across the United States come to Harvard University for 10 weeks over the summer to learn first-hand about the research process, and engage with the larger research community at Harvard. Working in small teams, students use computation and mathematical tools to tackle projects posed by Harvard faculty and industrial partners.