Skills in computational and applied mathematics prove invaluable to every quantitative discipline. Many students would benefit from a richer foundation in applied mathematics.

A **minor in Computational and Applied mathematics at Rice University** will provide you a core of knowledge in the discipline while allowing you sufficient flexibility to tailor your curriculum to complement your major interests. This program of study particularly appeals to students majoring in engineering, natural sciences, and economics.

For an overview of the Minor in Computational and Applied Mathematics, learning outcomes, requirements and more, visit General Announcements.

## Courses

All students will take courses in basic mathematical modeling and computing (CAAM 210) and linear algebra (CAAM 335), along with a course in either partial differential equations (CAAM 336) or optimization/operations research (CAAM 378).

Students then will select three more courses that could, for example, focus on computational science and engineering, differential equations or optimization; see the samples below.

## Requirements

To obtain a CAAM minor, students must complete at least 18 credit hours in the CAAM department, including:

- CAAM 210 Introduction to Engineering Computation
- CAAM 334 Matrix Analysis for Data Science
**(or)**CAAM 335 Matrix Analysis- CAAM 336 Differential Equations in Science and Engineering
**(or)**CAAM 378 Introduction to Operations Research and Optimization- Three additional 3-credit CAAM classes, at least two of which must be at the 400 level or above

## Sample Programs of Study

These programs are only suggestions. Students are encouraged to consult with a CAAM undergraduate advisor to tailor a program that best meets their interests.

### Computational Science and Engineering

- CAAM 210 Introduction to Engineering Computation
- CAAM 334 Matrix Analysis for Data Science
**(or)**CAAM 335 Matrix Analysis- CAAM 336 Differential Equations in Science and Engineering
- CAAM 453 Numerical Analysis I
- CAAM 519 Computational Science I
- CAAM 520 Computational Science II

### Differential Equations

- CAAM 210 Introduction to Engineering Computation
- CAAM 334 Matrix Analysis for Data Science
**(or)**CAAM 335 Matrix Analysis- CAAM 336 Differential Equations in Science and Engineering
- CAAM 453 Numerical Analysis I
- CAAM 415 Theoretical Neuroscience
- or CAAM 423 Partial Differential Equations I
- CAAM 536 Numerical Methods for Partial Differential Equations

### Optimization

- CAAM 210 Introduction to Engineering Computation
- CAAM 334 Matrix Analysis for Data Science
**(or)**CAAM 335 Matrix Analysis- CAAM 378 Introduction to Operations Research and Optimization
- CAAM 471 Introduction to Linear and Integer Programming
- CAAM 570 Graph Theory
- CAAM 574 Combinatorial Optimization