The M.S. in Scientific Computing, offered jointly by the Departments of Mathematics and of Computer Science, provides broad yet rigorous training in areas of mathematics and computer science related to scientific computing. It aims to prepare people with the right talents and background for a technical career doing practical computing. The program accommodates both full-time and part-time students, with most courses meeting in the evening. The program is self-contained and terminal, providing a complete set of skills in a field where the need is greater than the supply. The program focuses on the mathematics and computer science related to advanced computer modeling and simulation, and is similar in structure to terminal master’s programs in engineering, combining classroom training with practical experience. The coursework ranges from foundational mathematics and fundamental algorithms to such practical topics as data visualization and software tools. Elective courses encourage the exploration of specific application areas such as mathematical and statistical finance, applications of machine learning, fluid mechanics, finite element methods, and biomedical modeling. The program culminates in a master’s project, which serves to integrate the classroom material.
Coursework: A candidate for a master’s degree in scientific computing must accrue 36 points of course credit comprised of: 4 core courses (12 points) in mathematics, MATH-GA 2010, Numerical Methods I, MATH-GA 2020, Numerical Methods II, plus two of the following: MATH-GA 2701, Methods of Applied Mathematics, MATH-GA 2490, Partial Differential Equations I, MATH-GA 2702, Fluid Dynamics, MATH-GA 2962, Mathematical Statistics, MATH-GA 2704, Applied Stochastic Analysis, and DS-GA 1002, Statistical and Mathematical Methods; 4 core courses (12 points) in computer science, CSCI-GA 1170, Fundamental Algorithms, CSCI-GA 2110, Programming Languages, plus two of the following: CSCI-GA 2246, Open Source Tools, CSCI-GA 2270, Computer Graphics, CSCI-GA 2565, Machine Learning, CSCI-GA 2566, Foundations of Machine Learning, DS-GA 1001, Introduction to Data Science, DS-GA 1003, Machine Learning and Computational Statistics, DS-GA 1004, Big Data; 3 elective courses (9 points); and a capstone project course (3 points). Students with exceptional backgrounds may petition the program director for permission to substitute other appropriate courses for core courses.
Capstone Project: The master’s program culminates in a capstone project (3 points), which is usually taken during the final year of study. During the project, students go through the entire process of solving a real-world problem, from collecting and processing data to designing and fully implementing a solution. Courses that meet the capstone requirement must involve a significant software development component as well as a research component solving a realistic problem. A list of courses approved to meet the capstone requirement will be announced each academic year based on current course offerings. Advanced students can obtain permission from the director of the program to do an individual capstone project (3 points) under the supervision of a faculty member. Advanced students interested in pursuing further academic training may be permitted to do a master’s thesis (6 points) as an alternative to the master’s capstone project.