All Ph.D. students must take at least four elective courses, two taken from List A and two taken from either List A or B. All M.S. students must take at least two elective courses, one of them taken from List A below and one from either List A or C.
Elective List A: Statistical Science elective courses offered by the Statistics Department and open to M.S. and Ph.D. students
| Elective | Credits |
| STAT 207 - Intermediate Bayesian Statistics ** | 5 |
| STAT 209 - Generalized Linear Models ** | 5 |
| STAT 222 - Bayesian Nonparametric Methods | 5 |
| STAT 223 - Time Series Analysis | 5 |
| STAT 224 - Bayesian Survival Analysis and Clinical Trial Design | 5 |
| STAT 225 - Multivariate Statistical Methods | 5 |
| STAT 226 - Spatial Statistics | 5 |
| STAT 229 - Advanced Bayesian Computation | 5 |
| STAT 243 - Stochastic Processes | 5 |
| STAT 244 - Bayesian Decision Theory | 5 |
| STAT 246 - Probability Theory with Markov Chains | 5 |
** Required for Ph.D. students, elective for M.S. students
Elective List B: Statistical Science elective courses offered across campus available to Ph.D. students
| Elective | Credits |
| AM 216 - Stochastic Differential Equations | 5 |
| AM 230 - Numerical Optimization | 5 |
| AM 250 - An Introduction to High Performance Computing | 5 |
| CSE 207 - Random Process Models in Engineering | 5 |
| CSE 242 - Machine Learning | 5 |
| CSE 245 - Data Mining | 5 |
| CSE 251 - Large-scale Web Analytics and Machine Learning | 5 |
| CSE 260 - Information Retrieval | 5 |
| ECE 253 - Introduction to Information Theory | 5 |
| ECE 262 - Statistical Signal Processing | 5 |
| ECON 211A/B - Advanced Econometrics | 5 |
| ENVS 215A/L - Geographic Information Systems And Environmental Applications | 5 |
| MATH 204 - Analysis I | 5 |
| MATH 205 - Analysis II | 5 |
| MATH 208 - Manifolds I | 5 |
Elective List C: Statistical Science elective courses offered across campus available to M.S. students
| Elective | Credits |
| AM 216 - Stochastic Differential Equations | 5 |
| AM 230 - Numerical Optimization | 5 |
| AM 250 - An Introduction to High Performance Computing | 5 |
| CSE 207 - Random Process Models in Engineering | 5 |
| CSE 242 - Machine Learning | 5 |
| CSE 245 - Data Mining | 5 |
| CSE 251 - Large-scale Web Analytics and Machine Learning | 5 |
| CSE 260 - Information Retrieval | 5 |
| CSE 261 - Advanced Visualization | 5 |
| CSE 263 - Data-driven Discovery and Visualization | 5 |
| ECE 253 - Introduction to Information Theory | 5 |
| ECE 262 - Statistical Signal Processing | 5 |
| ECON 211A/B - Advanced Econometrics | 5 |
| ENVS 215A/L - Geographic Information Systems And Environmental Applications | 5 |