Required core
| Course | Title | Area |
|---|---|---|
| Statistics 400 | Introduction to Probability Models | Probability |
| Statistics 401 | Survey of Methods in Modern Statistics | Methods |
| Statistics 402 | Applied Regression | Modeling |
| Statistics 403 | Mathematical Statistics | Theory |
| Statistics 404 | Statistical Computing and Programming | Computing |
| Statistics 405 | Data Management | Data |
| Statistics 421 | Advanced Statistical Communication | Communication |
Elective options
MASDS students complete at least four 4-unit elective courses. Electives must follow MASDS rules and may include approved 400-level statistics courses, certain 200-level courses, internship credit, or individual study.
| Course | Title |
|---|---|
| Statistics 411 | Multivariate Statistical Analysis |
| Statistics 412 | Advanced Regression and Predictive Modeling |
| Statistics 413 | Machine Learning and Artificial Intelligence |
| Statistics 414 | From Predictive Artificial Intelligence to Generative Artificial Intelligence |
| Statistics 415 | Introduction to Forecasting |
| Statistics 416 | Spatial-Temporal Point Processes and Geostatistical Applications |
| Statistics 417 | Models in Finance |
| Statistics 418 | Tools in Data Science |
| Statistics 419 | Experimental Design |
| Statistics 420 | Causal Inference |
| Statistics 422 | Data Visualization |
| Statistics 423 | Longitudinal Data Analysis |
| Statistics 424 | Teamwork & Leadership in Data Science |
| Statistics 425 | Large Language Models in Text Mining |
| Statistics 426 | Deep Learning |
| Statistics 427 | Applied Bayesian Statistics |
| Statistics 428 | Applied SQL for Data Science |
Elective rules
Example course timelines
Schedule disclaimer: These examples illustrate possible pacing only. Course offerings and the quarters in which courses are offered vary from year to year. Students should confirm the current schedule with the MASDS program before planning enrollment.
Thesis work is completed with faculty guidance and does not add course units in the sample schedules below.
| Term | Sample courses |
|---|---|
| Fall Year 1 | Stats 400, Stats 401 |
| Winter Year 1 | Stats 402, Stats 403 |
| Spring Year 1 | Stats 404, Stats 405 |
| Fall Year 2 | Stats 421, Elective 1, thesis work |
| Winter Year 2 | Elective 2, Elective 3, thesis work |
| Spring Year 2 | Elective 4, thesis work |
| Term | Sample courses |
|---|---|
| Fall Year 1 | Stats 401 |
| Winter Year 1 | Stats 402 |
| Spring Year 1 | Stats 404 |
| Fall Year 2 | Stats 400, Stats 421 |
| Winter Year 2 | Stats 403 |
| Spring Year 2 | Stats 405 |
| Fall Year 3 | Elective 1 |
| Winter Year 3 | Elective 2, thesis work |
| Spring Year 3 | Elective 3, thesis work |
| Fall Year 4 | Elective 4, thesis work |
Selected 200-level replacements
Some 200-level courses replace similar 400-level courses. These courses are usually taken by PhD students and are generally taught during the day. Students may not take both the 200-level course and its corresponding 400-level equivalent.
| 200-level course | Similar 400-level course |
|---|---|
| 211 Topics in Econ and Machine Learning | Similar to 425 |
| M231A Pattern Recognition and Machine Learning | Similar to 413 |
| 240 Multivariate Analysis | Similar to 411 |
| 256 Causality | Similar to 420 |
| C161/C261 Introduction to Pattern Recognition and Machine Learning | Similar to 414 |