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Electives

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.