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Program Description
The MS in Scientific Computing, offered jointly by the Departments of Mathematics and of Computer Science, provides broad rigorous training in areas of mathematics and computer science related to scientific computing. It aims to prepare people for a technical career in scientific computing or for further study in a field with computing as an essential element.
The program accommodates both full-time and part-time students, with most courses meeting in the evening. Required coursework includes core mathematical and computer science material related to scientific computing. Students choose electives specific to their interest and goals. Specific application areas include mathematical and statistical finance, machine learning/data science, fluid mechanics, finite element methods, and biomedical modeling. The program culminates in a capstone project, which serves to integrate the classroom material.
Admissions
All applicants to the Graduate School of Arts and Science (GSAS) are required to submit the general application requirements, which include:
See Mathematics for admission requirements and instructions specific to this program.
Program Requirements
The program requires the completion of 36 credits, comprised of the following:
Course List
| Course |
Title |
Credits |
| MATH-GA 2010 | Numerical Methods I | 3 |
| MATH-GA 2020 | Numerical Methods II | 3 |
| 6 |
| Methods of Applied Mathematics | |
| Introduction to Partial Differential Equations | |
| Fluid Dynamics | |
| Applied Stochastic Analysis | |
| Mathematical Statistics | |
| Multivariable Analysis | |
| CSCI-GA 1170 | Fundamental Algorithms | 3 |
| CSCI-GA 2110 | Programming Languages | 3 |
| 6 |
CSCI-GA 2246 | | |
| Computer Graphics | |
| Machine Learning | |
| Foundations of Machine Learning | |
| Introduction to Data Science | |
| Machine Learning | |
| Big Data | |
| 9 |
| 3 |
| Total Credits | 36 |
Additional Program Requirements
Master's Project
The program culminates in a master’s project, which serves to integrate the classroom material.
Learning Outcomes
Upon successful completion of the program, graduates will have:
- Acquired technical skills suitable for employment in research and development, whether in academia, government, or industry. The demand for the right combination of skills exceeds the supply; the student shall acquire both the mathematical facility and the computational skills required for working in a team on scientific investigations or in the development of scientifically engineered products.
- Acquired a working knowledge of basic numerical methods.
- Become acquainted with the numerical solution of differential equations, including ordinary differential equations and elementary partial differential equations.
- An understanding of elementary asymptotic analysis, including simple scaling arguments and dimensional analysis.
- Familiarity with fundamental concepts of optimization, as illustrated by linear programming, convex programming, gradient-based optimization (such as Newton methods), or the calculus of variations.
- Acquired a working knowledge of basic algorithms as typically encountered in computer-science courses introducing the fundamental data structures.
- The ability to program or script in multiple languages, as well as understand various paradigms for the design of programming and scripting languages.
- A working knowledge of the most common types of programming tools, including those available in a major operating system such as Unix.
- Experience with mathematical and numerical modeling, as well as their appearance in various scientific applications.
Policies
NYU Policies
University-wide policies can be found on the New York University Policy pages.
Graduate School of Arts and Science Policies
Academic Policies for the Graduate School of Arts and Science can be found on the Academic Policies page.