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Current Courses

  • Autumn Quarter starts Monday, September 25, 2017.
  • Winter Quarter starts Monday, January 8, 2018
  • Spring Quarter starts Monday, April 2, 2018
  • Summer Quarter starts Monday, June 25, 2018
See Stanford's Academic Calendar 2017-2018.

Note to Faculty: To setup your course on Canvas, please visit the Instructor Guide. The Stanford Canvas Team is available for course setup consultations during the month of February (MWF, 10-Noon, 2-4pm), email help@stanfordcanvas.zendesk.com to make an appointment. For email lists and other services that support teaching and learning, visit Technology Support for Courses.

Titlesort descending Instructor(s) Quarter Day, Time, Location
A Course in Bayesian Statistics (STATS 270)
STATS 370 (section 1)

This course will treat Bayesian statistics at a relatively advanced level. Assuming familiarity with standard probability and multivariate distribution theory, we will provide a discussion of the mathematical and theoretical foundation for Bayesian inferential procedures. In particular, we will...

Wong, W., Orenstein, P. 2017-2018 Winter
Tuesday Thursday
3:00pm - 4:20pm
Green Earth Sciences150
A Course in Bayesian Statistics (STATS 370)
STATS 270 (section 1)

This course will treat Bayesian statistics at a relatively advanced level. Assuming familiarity with standard probability and multivariate distribution theory, we will provide a discussion of the mathematical and theoretical foundation for Bayesian inferential procedures. In particular, we will...

Wong, W., Orenstein, P. 2017-2018 Winter
Tuesday Thursday
3:00pm - 4:20pm
Green Earth Sciences150
Advanced Statistical Methods for Observational Studies (CHPR 290, EDUC 260B, HRP 292)
STATS 266 (section 1)

Design principles and statistical methods for observational studies. Topics include: matching methods, sensitivity analysis, and instrumental variables. 3 unit registration requires a small project and presentation. Computing is in R. Pre-requisites: HRP 261 and 262 or STAT 209 ( HRP 239), or...

Rogosa, D., Baiocchi, M. 2017-2018 Spring
Monday
2:30pm - 4:20pm
MSOBX303
Biostatistics (BIO 141)
STATS 141 (section 1)

Introductory statistical methods for biological data: describing data (numerical and graphical summaries); introduction to probability; and statistical inference (hypothesis tests and confidence intervals). Intermediate statistical methods: comparing groups (analysis of variance); analyzing...

Du, W., Zhu, X., Deng, B., Han, X., Tuzhilina, E. 2017-2018 Winter
Tuesday Thursday
9:00am - 10:20am
200-002
Computing for Data Science
STATS 290 (section 1)

Programming and computing techniques for the requirements of data science: acquisition and organization of data; visualization, modelling and inference for scientific applications; presentation and interactive communication of results. Emphasis on computing for substantial projects. Software...

Qian, J., Liu, K., Ignatiadis, N., Chambers, J., Narasimhan, B. 2017-2018 Winter
Monday Wednesday Friday
10:30am - 11:20am
NVIDIA Auditorium
Consulting Workshop
STATS 390 (section 1)

Skills required of practicing statistical consultants, including exposure to statistical applications. Students participate as consultants in the department's drop-in consulting service, analyze client data, and prepare formal written reports. Seminar provides supervised experience in short term...

Siegmund, D. 2017-2018 Spring
Friday
12:30pm - 1:20pm
Sequoia Hall 200
Consulting Workshop
STATS 390 (section 1)

Skills required of practicing statistical consultants, including exposure to statistical applications. Students participate as consultants in the department's drop-in consulting service, analyze client data, and prepare formal written reports. Seminar provides supervised experience in short term...

Johndrow, J. 2017-2018 Winter
Friday
12:30pm - 1:20pm
Sequoia Hall 200
Data Science 101
STATS 101 (section 1)

http://web.stanford.edu/class/stats101/ . This course will provide a hands-on introduction to statistics and data science. Students will engage with the fundamental ideas in inferential and computational thinking. Each week, we will explore...

Sabatti, C., Walther, G. 2017-2018 Spring
Monday Tuesday Wednesday Thursday Friday
9:30am - 10:20am
300-300
Information Theory (EE 376A)
STATS 376A (section 1)

The fundamental ideas of information theory. Entropy and intrinsic randomness. Data compression to the entropy limit. Huffman coding. Arithmetic coding. Channel capacity, the communication limit. Gaussian channels. Kolmogorov complexity. Asymptotic equipartition property. Information theory and...

Weissman, T., Han, Y., Tatwawadi, K. 2017-2018 Winter
Tuesday Thursday
12:00pm - 1:20pm
Gates B1
Intermediate Biostatistics: Analysis of Discrete Data (BIOMEDIN 233, HRP 261)
STATS 261 (section 1)

Methods for analyzing data from case-control and cross-sectional studies: the 2x2 table, chi-square test, Fisher's exact test, odds ratios, Mantel-Haenzel methods, stratification, tests for matched data, logistic regression, conditional logistic regression. Emphasis is on data analysis in SAS....

Sainani, K. 2017-2018 Winter
Monday Wednesday
11:30am - 12:50pm
Alway Building, Room M112
Intermediate Biostatistics: Regression, Prediction, Survival Analysis (HRP 262)
STATS 262 (section 1)

Methods for analyzing longitudinal data. Topics include Kaplan-Meier methods, Cox regression, hazard ratios, time-dependent variables, longitudinal data structures, profile plots, missing data, modeling change, MANOVA, repeated-measures ANOVA, GEE, and mixed models. Emphasis is on practical...

Sainani, K. 2017-2018 Spring
Monday Wednesday
1:30pm - 2:50pm
Li Ka Shing Center, room 120
Introduction to Applied Statistics
STATS 191 (section 1)

Statistical tools for modern data analysis. Topics include regression and prediction, elements of the analysis of variance, bootstrap, and cross-validation. Emphasis is on conceptual rather than theoretical understanding. Applications to social/biological sciences. Student assignments/projects...

Hwang, J., Zhang, Y., Ghosh, S., GAO, Z., Walther, G. 2017-2018 Winter
Monday Wednesday Friday
9:30am - 10:20am
Gates B1
Introduction to R (CME 195)
STATS 195 (section 1)

This short course runs for four weeks beginning in the second week of the quarter and is offered in fall and spring. It is recommended for students who want to use R in statistics, science, or engineering courses and for students who want to learn the basics of R programming. The goal of the...

Sesia, M. 2017-2018 Spring
Tuesday Thursday
12:00pm - 1:20pm
Hewlett Teaching Center Rm 101
Introduction to Regression Models and Analysis of Variance
STATS 203 (section 1)

Modeling and interpretation of observational and experimental data using linear and nonlinear regression methods. Model building and selection methods. Multivariable analysis. Fixed and random effects models. Experimental design. Pre- or corequisite: 200.

Johndrow, J., Greaves, D., Gupta, S. 2017-2018 Winter
Tuesday Thursday
1:30pm - 2:50pm
200-303
Introduction to Regression Models and Analysis of Variance
STATS 203 (section 1)

Modeling and interpretation of observational and experimental data using linear and nonlinear regression methods. Model building and selection methods. Multivariable analysis. Fixed and random effects models. Experimental design. Pre- or corequisite: 200.

Johndrow, J., Greaves, D., Gupta, S. 2017-2018 Winter
Tuesday Thursday
1:30pm - 2:50pm
200-303
Introduction to Regression Models and Analysis of Variance
STATS 203 (section 1)

Modeling and interpretation of observational and experimental data using linear and nonlinear regression methods. Model building and selection methods. Multivariable analysis. Fixed and random effects models. Experimental design. Pre- or corequisite: 200.

Johndrow, J. 2017-2018 Winter
Tuesday Thursday
1:30pm - 2:50pm
200-303
Introduction to Statistical Inference
STATS 200 (section 1)

Modern statistical concepts and procedures derived from a mathematical framework. Statistical inference, decision theory; point and interval estimation, tests of hypotheses; Neyman-Pearson theory. Bayesian analysis; maximum likelihood, large sample theory. Prerequisite: 116....

Sabatti, C., Bi, N., Mohanty, P., Ren, Z., Misiakiewicz, T., Li, S. 2017-2018 Winter
Monday Wednesday Friday
11:30am - 12:20pm
370-370
Introduction to Statistical Learning
STATS 216 (section 1)

Overview of supervised learning, with a focus on regression and classification methods. Syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis;cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso);...

Tibshirani, R., Tay, J., Feldman, M., Walsh, D., Donnat, C., Zhao, Q. 2017-2018 Winter
Monday Wednesday
3:00pm - 4:20pm
Gates B1
Introduction to Statistical Methods: Precalculus (PSYCH 10, STATS 160)
STATS 60 (section 1)

Techniques for organizing data, computing, and interpreting measures of central tendency, variability, and association. Estimation, confidence intervals, tests of hypotheses, t-tests, correlation, and regression. Possible topics: analysis of variance and chi-square tests, computer statistical...

Poldrack, R. 2017-2018 Winter
Monday Wednesday Friday
10:30am - 11:50am
420-040
Introduction to Statistical Methods: Precalculus (PSYCH 10, STATS 160)
STATS 60 (section 1)

Techniques for organizing data, computing, and interpreting measures of central tendency, variability, and association. Estimation, confidence intervals, tests of hypotheses, t-tests, correlation, and regression. Possible topics: analysis of variance and chi-square tests, computer statistical...

Xia, L. 2017-2018 Spring
Monday Tuesday Wednesday Thursday Friday
9:30am - 10:20am
420-040
Introduction to Statistical Methods: Precalculus (PSYCH 10, STATS 60)
STATS 160 (section 1)

Techniques for organizing data, computing, and interpreting measures of central tendency, variability, and association. Estimation, confidence intervals, tests of hypotheses, t-tests, correlation, and regression. Possible topics: analysis of variance and chi-square tests, computer statistical...

Xia, L. 2017-2018 Spring
Monday Tuesday Wednesday Thursday Friday
9:30am - 10:20am
420-040
Introduction to Statistical Methods: Precalculus (PSYCH 10, STATS 60)
STATS 160 (section 1)

Techniques for organizing data, computing, and interpreting measures of central tendency, variability, and association. Estimation, confidence intervals, tests of hypotheses, t-tests, correlation, and regression. Possible topics: analysis of variance and chi-square tests, computer statistical...

Poldrack, R. 2017-2018 Winter
Monday Wednesday Friday
10:30am - 11:50am
420-040
Introduction to Stochastic Processes I
STATS 217 (section 1)

Discrete and continuous time Markov chains, poisson processes, random walks, branching processes, first passage times, recurrence and transience, stationary distributions. Non-Statistics masters students may want to consider taking STATS 215 instead. Prerequisite: STATS 116 or consent of...

Tsao, A., Zhang, Y., Cao, S. 2017-2018 Winter
Monday Wednesday Friday
9:30am - 10:20am
200-205
Introduction to Stochastic Processes I
STATS 217 (section 1)

Discrete and continuous time Markov chains, poisson processes, random walks, branching processes, first passage times, recurrence and transience, stationary distributions. Non-Statistics masters students may want to consider taking STATS 215 instead. Prerequisite: STATS 116 or consent of...

2017-2018 Winter
Monday Wednesday Friday
9:30am - 10:20am
200-205
Introduction to Stochastic Processes II
STATS 218 (section 1)

Renewal theory, Brownian motion, Gaussian processes, second order processes, martingales.

Chatterjee, S. 2017-2018 Spring
Tuesday Thursday
10:30am - 11:50am
Sequoia Hall 200
Introduction to the Bootstrap
STATS 208 (section 1)

The bootstrap is a computer-based method for assigning measures of accuracy to statistical estimates. By substituting computation in place of mathematical formulas, it permits the statistical analysis of complicated estimators. Topics: nonparametric assessment of standard errors, biases, and...

Donoho, D. 2017-2018 Spring
Tuesday Thursday
9:00am - 10:20am
50-52H
Introduction to Time Series Analysis
STATS 207 (section 1)

Time series models used in economics and engineering. Trend fitting, autoregressive and moving average models and spectral analysis, Kalman filtering, and state-space models. Seasonality, transformations, and introduction to financial time series. Prerequisite: basic course in Statistics at the...

Donoho, D. 2017-2018 Spring
Tuesday Thursday
1:30pm - 2:50pm
380-380C
Literature of Statistics
STATS 319 (section 1)

Literature study of topics in statistics and probability culminating in oral and written reports. May be repeated for credit.

Romano, J. 2017-2018 Spring
Wednesday
11:30am - 12:20pm
Gates B12
Literature of Statistics
STATS 319 (section 1)

Literature study of topics in statistics and probability culminating in oral and written reports. May be repeated for credit.

Siegmund, D. 2017-2018 Winter
Monday
1:30pm - 2:50pm
380-381T
Mathematical Finance (MATH 238)
STATS 250 (section 1)

Stochastic models of financial markets. Forward and futures contracts. European options and equivalent martingale measures. Hedging strategies and management of risk. Term structure models and interest rate derivatives. Optimal stopping and American options. Corequisites: MATH 236 and 227 or...

Papanicolaou, G. 2017-2018 Winter
Tuesday Thursday
1:30pm - 2:50pm
380-380W
Mathematics and Statistics of Gambling (MATH 231)
STATS 334 (section 1)

Probability and statistics are founded on the study of games of chance. Nowadays, gambling (in casinos, sports and the Internet) is a huge business. This course addresses practical and theoretical aspects. Topics covered: mathematics of basic random phenomena (physics of coin tossing and...

Diaconis, P. 2017-2018 Spring
Tuesday Thursday
3:00pm - 4:20pm
200-030
Mathematics of Sports (MCS 100)
STATS 50 (section 1)

The use of mathematics, statistics, and probability in the analysis of sports performance, sports records, and strategy. Topics include mathematical analysis of the physics of sports and the determinations of optimal strategies. New diagnostic statistics and strategies for each sport....

DiCiccio, C. 2017-2018 Spring
Monday Wednesday Friday
2:30pm - 3:20pm
380-380F
Meta-research: Appraising Research Findings, Bias, and Meta-analysis (CHPR 206, HRP 206, MED 206)
STATS 211 (section 1)

Open to graduate, medical, and undergraduate students. Appraisal of the quality and credibility of research findings; evaluation of sources of bias. Meta-analysis as a quantitative (statistical) method for combining results of independent studies. Examples from medicine, epidemiology, genomics,...

Serghiou, S., Ioannidis, J. 2017-2018 Winter
Friday
9:30am - 12:20pm
60-109
Methods for Applied Statistics I: Exponential Families in Theory and Practice
STATS 305B (section 1)

Exponential families are central to parametric statistical inference. This course emphasizes the applied aspects of exponential family theory, with special emphasis on Generalized Linear Models. Prerequisite: 305A or equivalent. (NB: prior to 2016-17 the 305ABC series was numbered as 305, 306A...

Efron, B., Cai, F., Roquero Gimenez, J. 2017-2018 Winter
Monday Wednesday Friday
1:30pm - 2:20pm
Sequoia Hall 200
Methods for Applied Statistics II: Applied Multivariate Statistics
STATS 305C (section 1)

Theory, computational aspects, and practice of a variety of important multivariate statistical tools for data analysis. Topics include classical multivariate Gaussian and undirected graphical models, graphical displays. PCA, SVD and generalizations including canonical correlation analysis,...

Hastie, T. 2017-2018 Spring
Monday Wednesday
11:30am - 1:20pm
Sequoia Hall 200
Modern Applied Statistics: Data Mining
STATS 315B (section 1)

Two-part sequence. New techniques for predictive and descriptive learning using ideas that bridge gaps among statistics, computer science, and artificial intelligence. Emphasis is on statistical aspects of their application and integration with more standard statistical methodology. Predictive...

Friedman, J. 2017-2018 Spring
Tuesday Thursday
1:30pm - 2:50pm
Gates B1
Modern Applied Statistics: Learning
STATS 315A (section 1)

Overview of supervised learning. Linear regression and related methods. Model selection, least angle regression and the lasso, stepwise methods. Classification. Linear discriminant analysis, logistic regression, and support vector machines (SVMs). Basis expansions, splines and regularization....

Tibshirani, R., Friedberg, R., Arthur, J., Markovic, J., Katsevich, G., Sesia, M. 2017-2018 Winter
Monday Wednesday
1:30pm - 2:50pm
260-113
Multilevel Modeling Using R (EDUC 401D)
STATS 196A (section 1)

See http://rogosateaching.com/stat196/ . Multilevel data analysis examples using R. Topics include: two-level nested data, growth curve modeling, generalized linear models for counts and categorical data, nonlinear models, three-level analyses....

Rogosa, D. 2017-2018 Spring
Wednesday
3:30pm - 5:20pm
Littlefield 104
Network Information Theory (EE 376B)
STATS 376B (section 1)

Network information theory deals with the fundamental limits on information flow in networks and the optimal coding schemes that achieve these limits. It aims to extend Shannon's point-to-point information theory and the Ford-Fulkerson max-flow min-cut theorem to networks with multiple sources...

El Gamal, A. 2017-2018 Spring
Tuesday Thursday
9:00am - 10:20am
Herrin T185
PhD First Year Student Workshop
STATS 303 (section 1)

For Statistics First Year PhD students only. Discussion of relevant topics in first year student courses, consultation with PhD advisor.

Candes, E. 2017-2018 Spring
Monday Wednesday
3:00pm - 4:20pm
380-380D
PhD First Year Student Workshop
STATS 303 (section 1)

For Statistics First Year PhD students only. Discussion of relevant topics in first year student courses, consultation with PhD advisor.

Candes, E. 2017-2018 Winter
Monday Wednesday
11:30am - 1:20pm
Sequoia Hall 200
PhD First Year Student Workshop
STATS 303 (section 1)

For Statistics First Year PhD students only. Discussion of relevant topics in first year student courses, consultation with PhD advisor.

Candes, E. 2017-2018 Spring
Monday Wednesday Friday
11:30am - 1:20pm
Sequoia Hall 200
PhD First Year Student Workshop
STATS 303 (section 1)

For Statistics First Year PhD students only. Discussion of relevant topics in first year student courses, consultation with PhD advisor.

Candes, E. 2017-2018 Winter
Monday Wednesday
11:30am - 1:20pm
Sequoia Hall 200
Readings in Applied Data Science
STATS 337 (section 1)

Weekly readings and discussion of applied data science topics. Data wrangling, tidy data, and database basics. Visualization for exploration and explanation. The intersection of software engineering and data science: continuous integration, unit testing, and documentation. Reproducible research...

Wickham, H. 2017-2018 Spring
Monday
1:30pm - 2:50pm
160-314
Statistical and Machine Learning Methods for Genomics (BIO 268, BIOMEDIN 245, CS 373, GENE 245)
STATS 345 (section 1)

Introduction to statistical and computational methods for genomics. Sample topics include: expectation maximization, hidden Markov model, Markov chain Monte Carlo, ensemble learning, probabilistic graphical models, kernel methods and other modern machine learning paradigms. Rationales and...

2017-2018 Spring
Monday
10:30am - 11:50am
Statistical Learning Theory (CS 229T)
STATS 231 (section 1)

How do we formalize what it means for an algorithm to learn from data? This course focuses on developing mathematical tools for answering this question. We will present various common learning algorithms and prove theoretical guarantees about them. Topics include classical asymptotics, method of...

Duchi, J. 2017-2018 Spring
Monday Wednesday
3:00pm - 4:20pm
200-034
Statistical Methods for Group Comparisons and Causal Inference (EDUC 260A, HRP 239)
STATS 209 (section 1)

See http://rogosateaching.com/stat209/. Critical examination of statistical methods in social science and life sciences applications, especially for cause and effect determinations. Topics: mediating and moderating variables, potential outcomes...

Rogosa, D., Sklar, M. 2017-2018 Winter
Wednesday Friday
2:30pm - 4:20pm
Sequoia Hall 200
Statistical Models in Biology
STATS 215 (section 1)

Poisson and renewal processes, Markov chains in discrete and continuous time, branching processes, diffusion. Applications to models of nucleotide evolution, recombination, the Wright-Fisher process, coalescence, genetic mapping, sequence analysis. Theoretical material approximately the same as...

Siegmund, D. 2017-2018 Spring
Tuesday Thursday
1:30pm - 2:50pm
Hewlett Teaching Center 103
Statistical Models in Genetics
STATS 367 (section 1)

This course will cover statistical problems in population genetics and molecular evolution with an emphasis on coalescent theory. Special attention will be paid to current research topics, illustrating the challenges presented by genomic data obtained via high-throughput technologies. No prior...

Palacios, J. 2017-2018 Winter
Tuesday Thursday
10:30am - 11:50am
STLC118
Stochastic Processes
STATS 317 (section 1)

Semimartingales, stochastic integration, Ito's formula, Girsanov's theorem. Gaussian and related processes. Stationary/isotropic processes. Integral geometry and geometric probability. Maxima of random fields and applications to spatial statistics and imaging.

Taylor, J. 2017-2018 Winter
Tuesday Thursday
1:30pm - 2:50pm
420-050

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