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Minor
MinorOverview
The undergraduate minor in Statistics is designed to complement major degree programs primarily in the social and natural sciences. Students with an undergraduate Statistics minor gain theoretical and practical training in statistical methods. The minor:
equips students with foundational knowledge in probability and statistics
teaches students to correctly choose and apply statistical methods
Program Learning Outcomes
Students are expected to be able to:
Select the appropriate statistical methodology for the problem at hand
Analyze statistical methodology regarding its appropriateness and limitations critically
Present and interpret the result of statistical analyses
Requirements
Program Requirements
Qualifying Courses
Note that CME 102 is not a suitable course to use in place of MATH 52; if a student has taken CME 102, they need to take one of the alternative courses to satisfy the Qualifying Course requirement.
MATH 52 can be double-counted between the statistics minor and a major or other minor.
Theoretical Statistics Requirement
STATS 118
Probability Theory for Statistical Inference
STATS 200
Introduction to Theoretical Statistics
STATS 200Q
Philosophical Foundations of Statistics
Students who take STATS 118 for this Theoretical Statistics Requirement can count STATS 200 or STATS 200Q toward the Elective Course requirement below.
If a student has taken MATH 151 or STATS 116X (as an Elective Course for the statistics minor or for any other program), they are required to take STATS 200 or STATS 200Q to fulfill the Theoretical Statistics Requirement.
STATS 118 has a prerequisite of STATS 117, which can count as an Elective Course below. CS 109, EE 178, and MS&E 120 also satisfy the prerequisite for STATS 118, but these classes may NOT count toward the Elective Courses requirement for the minor.
Elective Courses
Students must take 3 elective courses for a total of at least 9 units. (Each course must be taken for a minimum of 3 units.) At least 2 courses must be Advanced Statistics Electives (STATS courses numbered 200 and above), and up to 1 course may be from the lists of Additional Elective Options.
Advanced Statistics Electives
At least two electives (2 or 3 courses) must be STATS courses numbered 200 or higher.
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
Students who take STATS 118 for the Theoretical Statistics Requirement can count STATS 200 or STATS 200Q toward this Elective Course requirement.
A list of recommended courses can be found on the program website opens in a new tab.
Additional Elective Options
Students may fulfill up to one elective course (0 or 1 course) from this list of Additional Elective Options.
Courses
DATASCI 112
Principles of Data Science
DATASCI 120
Data Narratives
DATASCI 154
Data Science for Social Impact
DATASCI 156
Thinking and Making with Data
DATASCI 161
Causality, Decision Making and Data Science
DATASCI 190
The Data Science Experience
DATASCI 192A
Data Science Practicum I
DATASCI 192B
Data Science Practicum II
DATASCI 193
Applied Artistic and Cultural Analysis
DATASCI 194B
Data Science for Computational Molecular Biology
DATASCI 194C
Driving Innovation: Benchmarks, Competitions, and Challenge Problems in Machine Learning and Beyond
DATASCI 194D
The Data Science of Disinformation
DATASCI 194L
Data Science and the Science of Learning
DATASCI 194N
Data Science for Neuroscience
DATASCI 194W
Surfing the Waves of Data
DATASCI 196
Teaching Data Science
DATASCI 197
WiDS Datathon Independent Study
DATASCI 198
Practical Training
DATASCI 199
Independent Study
DATASCI 199W
Honors Capstone Thesis
DATASCI 200Q
Philosophical Foundations of Statistics
DATASCI 211
Accelerating Research with Marlowe: Practical GPU Computing for Scientists
DATASCI 294B
Data Science for Computational Molecular Biology
DATASCI 294C
Driving Innovation: Benchmarks, Competitions, and Challenge Problems in Machine Learning and Beyond
DATASCI 294D
The Data Science of Disinformation
DATASCI 294L
Data Science and the Science of Learning
DATASCI 294N
Data Science for Neuroscience
DATASCI 294W
Surfing the Waves of Data
Courses
MATH 151
Introduction to Probability Theory
STATS 100
Mathematics of Sports
STATS 110
Introduction to Statistics for Engineering and the Sciences
STATS 116X
Theory of Probability (accelerated)
STATS 117
Introduction to Probability Theory
STATS 141
Introduction to Statistics for Biology
If a student takes MATH 151 or STATS 116X, they are required to take STATS 200 or STATS 200Q to fulfill the Theoretical Statistics Requirement.
For any questions about program requirements, please reach out to the Student Services Specialist via email: datasciencemajor-inquiries@lists.stanford.edu.