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PhD Minor
PhD MinorOverview
Doctoral students pursuing a PhD degree in a program other than Statistics may apply for the PhD minor in Statistics. A minor is not a requirement for any degree but is available when agreed upon by the student and the major and minor department.
Requirements
Course Requirements
Must complete 30 units total from the following
Students must complete a total of 30 units for the Ph.D. Minor. All courses must be taken for a letter grade when offered (minimum grade of B for each course). 20 units must be from Statistics courses numbered 300 and above (not including 301, 302, 303, 398, or 399). The remaining 10 units of Statistics courses can be 200 level and above. The selection of courses must be approved by the Statistics department, and the application form must be approved by both the student's Ph.D. department and the Statistics department. The statistics Ph.D. minor only requires coursework; there is no project or thesis requirement.
For further information about the Statistics PhD degree program requirements, see the Statistics Department website opens in a new tab.
Courses
STATS 300A
Theory of Statistics I
STATS 300B
Theory of Statistics II
STATS 300C
Theory of Statistics III
STATS 301
Statistics Teaching Practicum
STATS 302
Qualifying Exams Workshop
STATS 303
Statistics Faculty Research Presentations
STATS 305A
Applied Statistics I
STATS 305B
Applied Statistics II
STATS 305C
Applied Statistics III
STATS 307
Time Series Analysis
STATS 310A
Theory of Probability I
STATS 310B
Theory of Probability II
STATS 310C
Theory of Probability III
STATS 311
Information Theory and Statistics
STATS 314A
Advanced Statistical Theory
STATS 315A
Modern Applied Statistics: Learning
STATS 315B
Modern Applied Statistics: Learning II
STATS 317
Stochastic Processes
STATS 318
Modern Markov Chains
STATS 319
Literature of Statistics
STATS 320
Machine Learning Methods for Neural Data Analysis
STATS 322
Function Estimation in White Noise
STATS 323
Sequential Analysis
STATS 324
Stein's Method
STATS 325
Multivariate Analysis and Random Matrices in Statistics
STATS 326
Topics in Generative Modeling Methods in Protein Modeling and Design
STATS 331
Survival Analysis
STATS 334
Mathematics and Statistics of Gambling
STATS 335
The Challenge Problems Paradigm in Empirical Machine Learning and Beyond
STATS 352
Topics in Computing for Data Science
STATS 354
Generalization and Causality in Biohealth
STATS 357
Reliability and Validity in Artificial Intelligence
STATS 361
Causal Inference
STATS 362
Topic: Monte Carlo
STATS 363
Design of Experiments
STATS 365
Empirical Likelihood
STATS 369
Methods from Statistical Physics
STATS 370
Bayesian Statistics
STATS 371
Applied Bayesian Statistics
STATS 375
Mathematical Problems in Machine Learning
STATS 390
Consulting Workshop
STATS 398
Industrial Research for Statisticians
STATS 399
Research
Courses must be taken for a letter grade with a minimum grade of B for each course.
Courses
STATS 200
Introduction to Theoretical Statistics
STATS 200Q
Philosophical Foundations of Statistics
STATS 202
Statistical Learning and Data Science
STATS 202F
Statistical Learning and Data Science [Flipped]
STATS 202V
Statistical Learning and Data Science [Virtual]
STATS 203
Regression Models and Analysis of Variance
STATS 204
Sampling
STATS 205
Introduction to Nonparametric Statistics
STATS 206
Applied Multivariate Analysis
STATS 207
Time Series Analysis
STATS 208
Resampling Methods: Bootstrap, Cross Validation and Beyond
STATS 209
Introduction to Causal Inference
STATS 211
Meta-research: Appraising Research Findings, Bias, and Meta-analysis
STATS 214
Machine Learning Theory
STATS 215
Statistical Models in Biology
STATS 217
Introduction to Stochastic Processes I
STATS 218
Introduction to Stochastic Processes II
STATS 219
Stochastic Processes
STATS 220
Machine Learning Methods for Neural Data Analysis
STATS 221
Random Processes on Graphs and Lattices
STATS 223
Sequential Analysis
STATS 229
Machine Learning
STATS 232
Machine Learning for Sequence Modeling
STATS 242
NeuroTech Training Seminar
STATS 248P
Causal Inference in Clinical Trials and Observational Study
STATS 250
Mathematical Finance
STATS 251
Causal Inference in Clinical Trials and Observational Studies
STATS 260A
Workshop in Biomedical Data Science
STATS 260B
Workshop in Biomedical Data Science
STATS 260C
Workshop in Biomedical Data Science
STATS 261
Intermediate Biostatistics: Analysis of Discrete Data
STATS 262
Intermediate Biostatistics: Regression, Prediction, Survival Analysis
STATS 263
Design of Experiments
STATS 264
Foundations of Statistical and Scientific Inference
STATS 270
Bayesian Statistics
STATS 271
Applied Bayesian Statistics
STATS 285
Massive Computational Experiments, Painlessly
STATS 290
Computing for Data Science
STATS 292
Statistical Models of Text and Language
STATS 298
Industrial Research for Statisticians
STATS 299
Independent Study
STATS 300A
Theory of Statistics I
STATS 300B
Theory of Statistics II
STATS 300C
Theory of Statistics III
STATS 301
Statistics Teaching Practicum
STATS 302
Qualifying Exams Workshop
STATS 303
Statistics Faculty Research Presentations
STATS 305A
Applied Statistics I
STATS 305B
Applied Statistics II
STATS 305C
Applied Statistics III
STATS 307
Time Series Analysis
STATS 310A
Theory of Probability I
STATS 310B
Theory of Probability II
STATS 310C
Theory of Probability III
STATS 311
Information Theory and Statistics
STATS 314A
Advanced Statistical Theory
STATS 315A
Modern Applied Statistics: Learning
STATS 315B
Modern Applied Statistics: Learning II
STATS 317
Stochastic Processes
STATS 318
Modern Markov Chains
STATS 319
Literature of Statistics
STATS 320
Machine Learning Methods for Neural Data Analysis
STATS 322
Function Estimation in White Noise
STATS 323
Sequential Analysis
STATS 324
Stein's Method
STATS 325
Multivariate Analysis and Random Matrices in Statistics
STATS 326
Topics in Generative Modeling Methods in Protein Modeling and Design
STATS 331
Survival Analysis
STATS 334
Mathematics and Statistics of Gambling
STATS 335
The Challenge Problems Paradigm in Empirical Machine Learning and Beyond
STATS 352
Topics in Computing for Data Science
STATS 354
Generalization and Causality in Biohealth
STATS 357
Reliability and Validity in Artificial Intelligence
STATS 361
Causal Inference
STATS 362
Topic: Monte Carlo
STATS 363
Design of Experiments
STATS 365
Empirical Likelihood
STATS 369
Methods from Statistical Physics
STATS 370
Bayesian Statistics
STATS 371
Applied Bayesian Statistics
STATS 375
Mathematical Problems in Machine Learning
STATS 390
Consulting Workshop
STATS 398
Industrial Research for Statisticians
STATS 399
Research
Courses must be taken for a letter grade with a minimum grade of B for each course.
Students may not include research units (299/399) or more than one unit of STATS 390, Consulting Workshop, toward the 30 units.
The Statistics department must approve the selection of courses and the PhD Minor Application. opens in a new tab The student’s PhD department and the Statistics department must both approve the minor form. After applying, students should email the Assistant Director of Student Services to follow up, attaching a copy of their unofficial Stanford transcript, highlighted with relevant courses and grades.
Note that, per Stanford's PhD minor policy, a Ph.D. Minor Application opens in a new tab eform is submitted at the time of (or after) admission to candidacy in the student's major department. Representation by Statistics, as the minor department, on the University oral exam committee is not required. All coursework for the minor must be completed at Stanford prior to a student moving to TGR status. Units taken for the minor can be counted as part of the overall requirement for the Ph.D. of 135 units of graduate course work. Courses used for a minor may not be used also to meet the requirements for a master's degree or for the completion of a different Ph.D. minor.