Stanford University

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.

Important notes:

  • All electives listed below are 9 units. 
  • 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.
  • ECON 102C Advanced Topics in Econometrics
  • ECON 140 Introduction to Financial Economics
  • ECON 160 Game Theory and Economic Applications
  • ECON 179 Experimental Economics
  • EE 261 The Fourier Transform and Its Applications
  • EE 263 Introduction to Linear Dynamical Systems
  • EE 278 Introduction to Statistical Signal Processing
  • EE 282 Computer Systems Architecture
  • EE 364A Convex Optimization I
  • EE 364B Convex Optimization II
  • PHIL 151 Metalogic
  • CME 206 Introduction to Numerical Methods for Engineering
  • CME 211 Software Development for Scientists and Engineers
  • CME 302 Numerical Linear Algebra
  • CS 108 Object-Oriented Systems Design
  • CS 110 Principles of Computer Systems OR CS 111 Operating Systems Principles
  • CS 140Operating Systems and Systems Programming
  • CS 143Compilers
  • CS 157 Logic and Automated Reasoning
  • CS 161 Design and Analysis of Algorithms
  • CS 194 Software Project
  • CS 221 Artificial Intelligence: Principles and Techniques
  • CS 223A Introduction to Robotics
  • CS 225A Experimental Robotics
  • CS 228 Probabilistic Graphical Models: Principles and Techniques
  • CS 229 Machine Learning
  • CS 243 Program Analysis and Optimizations
  • CS 246 Mining Massive Data Sets
  • CS 248 Interactive Computer Graphics
  • MS&E 220 Probabilistic Analysis
  • MS&E 223 Simulation
  • MS&E 226 "Small" Data
  • MS&E 251 Stochastic Control
  • MS&E 334 Topics in Social Data
  • MATH 104 Applied Matrix Theory
  • MATH 106 Functions of a Complex Variable
  • MATH 107 Graph Theory
  • MATH 108 Introduction to Combinatorics and Its Applications
  • MATH 113 Linear Algebra and Matrix Theory
  • MATH 114 Introduction to Scientific Computing
  • MATH 115 Functions of a Real Variable
  • MATH 116 Complex Analysis
  • MATH 131P Partial Differential Equations I
  • MATH 132 Partial Differential Equations II
  • MATH 136 Stochastic Processes
  • MATH 158 Basic Probability and Stochastic Processes with Engineering Applications
  • MATH 159 Discrete Probabilistic Methods
  • MATH 171 Fundamental Concepts of Analysis
  • MATH 172 Lebesgue Integration and Fourier Analysis
  • STATS 100 Mathematics of Sports
  • STATS 101 Data Science 101
  • STATS 202 Data Mining and Analysis
  • STATS 206 Applied Multivariate Analysis
  • STATS 207 Introduction to Time Series Analysis
  • STATS 208 Introduction to the Bootstrap
  • STATS 209 Statistical Methods for Group Comparisons and Causal Inference
  • STATS 215 Statistical Models in Biology
  • STATS 216 Introduction to Statistical Learning
  • STATS 217 Introduction to Stochastic Processes I
  • STATS 218 Introduction to Stochastic Processes II
  • STATS 219 Stochastic Processes
  • STATS 222 Statistical Methods for Longitudinal Research
  • STATS 240 Statistical Methods in Finance
  • STATS 270 Bayesian Statistics I