## Electives:

### Mathematical and Computational Science Electives

Choose three courses in Mathematical and Computational Science 100-level or above, at least 3 units each from two different departments. Electives not listed here should be reviewed via the Elective Approval Form

Undeclared students looking for an introduction to MCS may take Data Science 101 (STATS 101). If the student then declares the MCS major, STATS 101 may be used for elective credit toward the major.

MCS Program-approved electives: | Units | |
---|---|---|

9 units | ||

Advanced Topics in Econometrics | ||

Introduction to Financial Economics | ||

Game Theory and Economic Applications | ||

Experimental Economics | ||

The Fourier Transform and Its Applications | ||

Introduction to Linear Dynamical Systems | ||

Introduction to Statistical Signal Processing | ||

Computer Systems Architecture | ||

Convex Optimization I | ||

Convex Optimization II | ||

Probabilistic Analysis | ||

Simulation | ||

"Small" Data | ||

Stochastic Control | ||

MS&E 334 | Topics in Social Data | |

Mathematics of Sports | ||

Applied Matrix Theory | ||

Functions of a Complex Variable | ||

Graph Theory | ||

Introduction to Combinatorics and Its Applications | ||

Linear Algebra and Matrix Theory | ||

Introduction to Scientific Computing | ||

Functions of a Real Variable | ||

Complex Analysis | ||

Partial Differential Equations I | ||

MATH 132 | Partial Differential Equations II | |

Stochastic Processes | ||

MATH 158 | Basic Probability ans Stochastic Processes with Engineering Applications | |

Discrete Probabilistic Methods | ||

Fundamental Concepts of Analysis | ||

Lebesgue Integration and Fourier Analysis | ||

Metalogic | ||

Data Mining and Analysis | ||

Applied Multivariate Analysis | ||

Introduction to Time Series Analysis | ||

Introduction to the Bootstrap | ||

STATS 209 | Statistical Methods for Group Comparisons and Causal Inference | |

Statistical Models in Biology | ||

Introduction to Statistical Learning | ||

Introduction to Stochastic Processes I | ||

Introduction to Stochastic Processes II | ||

Stochastic Processes | ||

STATS 222 | Statistical Methods for Longitudinal Research | |

Statistical Methods in Finance | ||

Bayesian Statistics I | ||

For Computer Science (CS), electives can include courses not taken as units under the CS list above and the following: | ||

Introduction to Numerical Methods for Engineering | ||

Software Development for Scientists and Engineers | ||

Numerical Linear Algebra | ||

Object-Oriented Systems Design | ||

Principles of Computer Systems | ||

Operating Systems and Systems Programming | ||

Compilers | ||

Logic and Automated Reasoning | ||

Design and Analysis of Algorithms | ||

Software Project | ||

Artificial Intelligence: Principles and Techniques | ||

Introduction to Robotics | ||

Experimental Robotics | ||

Probabilistic Graphical Models: Principles and Techniques | ||

Machine Learning | ||

Program Analysis and Optimizations | ||

Mining Massive Data Sets | ||

Interactive Computer Graphics | ||

Electives that are not offered this year, but may be offered in subsequent years, are eligible for credit toward the major. | ||

With the adviser's approval, courses other than those offered by the sponsoring departments may be used to fulfill part of the elective requirement. Courses must provide skills relevant to the MCS degree and do not overlap courses in the student's program. Depending on student’s interests, these may be in fields such as, biology, economics, electrical engineering, industrial engineering, and medicine, are otherwise relevant to a mathematical sciences major. |