STATS 60: Introduction to Statistical Methods: Precalculus (PSYCH 10, STATS 160)
Techniques for organizing data, computing, and interpreting measures of central tendency, variability, and association. Estimation, confidence intervals, tests of hypotheses, ttests, correlation, and regression. Possible topics: analysis of variance and chisquare tests, computer statistical packages.
Terms: Aut, Win, Spr, Sum

Units: 5

UG Reqs: GER:DBMath, WAYAQR, WAYFR

Grading: Letter or Credit/No Credit
Instructors:
DiCiccio, C. (PI)
;
Khazenzon, A. (PI)
;
Leong, Y. (PI)
...
more instructors for STATS 60 »
Instructors:
DiCiccio, C. (PI)
;
Khazenzon, A. (PI)
;
Leong, Y. (PI)
;
Poldrack, R. (PI)
;
Schwartz, J. (PI)
;
Sklar, M. (PI)
;
Xia, L. (PI)
;
bonnen, t. (PI)
;
ten Brink, M. (PI)
;
Cao, S. (TA)
;
Greaves, D. (TA)
;
Guan, L. (TA)
;
Lemhadri, I. (TA)
;
Panigrahi, S. (TA)
;
Sesia, M. (TA)
STATS 101: Data Science 101
http://web.stanford.edu/class/stats101/ . This course will provide a handson introduction to statistics and data science. Students will engage with the fundamental ideas in inferential and computational thinking. Each week, we will explore a core topic comprising three lectures and two labs (a module), in which students will manipulate realworld data and learn about statistical and computational tools. Students will engage in statistical computing and visualization with current data analytic software (Jupyter, R). The objectives of this course are to have students (1) be able to connect data to underlying phenomena and to think critically about conclusions drawn from data analysis, and (2) be knowledgeable about programming abstractions so that they can later design their own computational inferential procedures. No programming or statistical background is assumed. Freshmen and sophomores interested in data science, computing and statistics are encouraged to attend. Open to graduates as well.
Terms: Aut, Spr, Sum

Units: 5

UG Reqs: GER: DBNatSci, WAYAQR

Grading: Letter or Credit/No Credit
Instructors:
Choi, A. (PI)
;
DiCiccio, C. (PI)
;
Mohanty, P. (PI)
...
more instructors for STATS 101 »
Instructors:
Choi, A. (PI)
;
DiCiccio, C. (PI)
;
Mohanty, P. (PI)
;
Sabatti, C. (PI)
;
Taylor, J. (PI)
;
Walther, G. (PI)
;
Xia, L. (PI)
;
Bhattacharya, S. (TA)
;
Du, W. (TA)
;
Misiakiewicz, T. (TA)
STATS 110: Statistical Methods in Engineering and the Physical Sciences
Introduction to statistics for engineers and physical scientists. Topics: descriptive statistics, probability, interval estimation, tests of hypotheses, nonparametric methods, linear regression, analysis of variance, elementary experimental design. Prerequisite: one year of calculus.
Terms: Aut, Sum

Units: 45

UG Reqs: GER:DBMath, WAYAQR, WAYFR

Grading: Letter or Credit/No Credit
Instructors:
Bi, N. (PI)
;
Miolane, N. (PI)
;
Pavlyshyn, D. (PI)
...
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STATS 116: Theory of Probability
Probability spaces as models for phenomena with statistical regularity. Discrete spaces (binomial, hypergeometric, Poisson). Continuous spaces (normal, exponential) and densities. Random variables, expectation, independence, conditional probability. Introduction to the laws of large numbers and central limit theorem. Prerequisites:
MATH 52 and familiarity with infinite series, or equivalent.
Terms: Aut, Spr, Sum

Units: 35

UG Reqs: GER:DBMath, WAYAQR, WAYFR

Grading: Letter or Credit/No Credit
Instructors:
Donoho, D. (PI)
;
Kaluwa Devage, P. (PI)
;
Zhang, Y. (PI)
...
more instructors for STATS 116 »
Instructors:
Donoho, D. (PI)
;
Kaluwa Devage, P. (PI)
;
Zhang, Y. (PI)
;
Bi, N. (TA)
;
Cauchois, M. (TA)
;
SUR, P. (TA)
;
YAN, J. (TA)
;
YANG, J. (TA)
;
Zhang, Y. (TA)
STATS 160: Introduction to Statistical Methods: Precalculus (PSYCH 10, STATS 60)
Techniques for organizing data, computing, and interpreting measures of central tendency, variability, and association. Estimation, confidence intervals, tests of hypotheses, ttests, correlation, and regression. Possible topics: analysis of variance and chisquare tests, computer statistical packages.
Terms: Aut, Win, Spr, Sum

Units: 5

Grading: Letter or Credit/No Credit
Instructors:
DiCiccio, C. (PI)
;
Poldrack, R. (PI)
;
Sklar, M. (PI)
...
more instructors for STATS 160 »
Instructors:
DiCiccio, C. (PI)
;
Poldrack, R. (PI)
;
Sklar, M. (PI)
;
Xia, L. (PI)
;
bonnen, t. (PI)
;
Cao, S. (TA)
;
Greaves, D. (TA)
;
Guan, L. (TA)
;
Lemhadri, I. (TA)
;
Panigrahi, S. (TA)
;
Sesia, M. (TA)
STATS 199: Independent Study
For undergraduates.
Terms: Aut, Win, Spr, Sum

Units: 115

Repeatable for credit

Grading: Satisfactory/No Credit
Instructors:
Baiocchi, M. (PI)
;
Candes, E. (PI)
;
Dembo, A. (PI)
...
more instructors for STATS 199 »
Instructors:
Baiocchi, M. (PI)
;
Candes, E. (PI)
;
Dembo, A. (PI)
;
Diaconis, P. (PI)
;
Donoho, D. (PI)
;
Duchi, J. (PI)
;
Efron, B. (PI)
;
Friedman, J. (PI)
;
Hastie, T. (PI)
;
Holmes, S. (PI)
;
Jackman, S. (PI)
;
Johnstone, I. (PI)
;
Lai, T. (PI)
;
Mackey, L. (PI)
;
Montanari, A. (PI)
;
Mukherjee, R. (PI)
;
Owen, A. (PI)
;
Palacios, J. (PI)
;
Rajaratnam, B. (PI)
;
Rogosa, D. (PI)
;
Romano, J. (PI)
;
Sabatti, C. (PI)
;
Siegmund, D. (PI)
;
Switzer, P. (PI)
;
Taylor, J. (PI)
;
Tibshirani, R. (PI)
;
Walther, G. (PI)
;
Wong, W. (PI)
STATS 202: Data Mining and Analysis
Data mining is used to discover patterns and relationships in data. Emphasis is on large complex data sets such as those in very large databases or through web mining. Topics: decision trees, association rules, clustering, case based methods, and data visualization. Prereqs: Introductory courses in statistics or probability (e.g.,
Stats 60), linear algebra (e.g.,
Math 51), and computer programming (e.g.,
CS 105).
Terms: Aut, Sum

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Patel, R. (PI)
;
Walther, G. (PI)
;
Feldman, M. (TA)
...
more instructors for STATS 202 »
Instructors:
Patel, R. (PI)
;
Walther, G. (PI)
;
Feldman, M. (TA)
;
Markovic, J. (TA)
;
Orenstein, P. (TA)
;
Qian, J. (TA)
;
Ruan, F. (TA)
;
Tsao, A. (TA)
;
Tuzhilina, E. (TA)
;
Zhang, Y. (TA)
STATS 216V: Introduction to Statistical Learning
Overview of supervised learning, with a focus on regression and classification methods. Syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; crossvalidation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; treebased methods, random forests and boosting; supportvector machines; Some unsupervised learning: principal components and clustering (kmeans and hierarchical). Computing is done in R, through tutorial sessions and homework assignments. This mathlight course is offered remotely only via video segments (MOOC style). TAs will host remote weekly office hours using an online platform such as Google Hangout or BlueJeans. There are four homework assignments, a midterm, and a final exam, all of which are administered remotely. Prereqs: Introductory courses in statistics or probability (e.g.,
Stats 60 or
Stats 101), linear algebra (e.g.,
Math 51), and computer programming (e.g.,
CS 105).
Terms: Sum

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Hastie, T. (PI)
STATS 217: Introduction to Stochastic Processes I
Discrete and continuous time Markov chains, poisson processes, random walks, branching processes, first passage times, recurrence and transience, stationary distributions. NonStatistics masters students may want to consider taking
STATS 215 instead. Prerequisite:
STATS 116 or consent of instructor.
Terms: Win, Sum

Units: 23

Grading: Letter or Credit/No Credit
STATS 237: Theory of Investment Portfolios and Derivative Securities
Asset returns and their volatilities. Markowitz portfolio theory, capital asset pricing model, multifactor pricing models. Measures of market risk. Financial derivatives and hedging. BlackScholes pricing of European options. Valuation of American options. Implied volatility and the Greeks. Prerequisite:
STATS 116 or equivalent
Terms: Sum

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Tsang, K. (PI)
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