STATS 32: Introduction to R for Undergraduates
This short course runs for weeks two through five of the quarter. It is recommended for undergraduate students who want to use R in the linguistics, humanities, social sciences or biological sciences and for students who want to learn the basics of R programming. The goal of the short course is to familiarize students with R's tools for scientific computing. Lectures will be interactive with a focus on learning by example, and assignments will be applicationdriven. No prior programming experience is needed. Topics covered include basic data structures, File I/O, graphs, control structures, etc, and some useful packages in R. Prerequisite: undergraduate student. Priority given to nonengineering students. Laptops necessary for use in class.
Terms: Aut

Units: 1

Grading: Satisfactory/No Credit
Instructors:
Tay, J. (PI)
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)
;
Xia, L. (PI)
;
bonnen, t. (PI)
;
ten Brink, M. (PI)
;
Cao, S. (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:
Mohanty, P. (PI)
;
Sabatti, C. (PI)
;
Taylor, J. (PI)
...
more instructors for STATS 101 »
Instructors:
Mohanty, P. (PI)
;
Sabatti, C. (PI)
;
Taylor, J. (PI)
;
Walther, G. (PI)
;
Xia, L. (PI)
;
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
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)
;
Cauchois, M. (TA)
;
SUR, P. (TA)
;
YAN, J. (TA)
;
YANG, J. (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)
;
Xia, L. (PI)
...
more instructors for STATS 160 »
Instructors:
DiCiccio, C. (PI)
;
Poldrack, R. (PI)
;
Xia, L. (PI)
;
bonnen, t. (PI)
;
Cao, S. (TA)
;
Panigrahi, S. (TA)
;
Sesia, M. (TA)
STATS 195: Introduction to R (CME 195)
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 short course is to familiarize students with R's tools for scientific computing. Lectures will be interactive with a focus on learning by example, and assignments will be applicationdriven. No prior programming experience is needed. Topics covered include basic data structures, File I/O, graphs, control structures, etc, and some useful packages in R.
Terms: Aut, Spr

Units: 1

Grading: Satisfactory/No Credit
Instructors:
Nguyen, L. (PI)
;
Sesia, M. (PI)
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 200: Introduction to Statistical Inference
Modern statistical concepts and procedures derived from a mathematical framework. Statistical inference, decision theory; point and interval estimation, tests of hypotheses; NeymanPearson theory. Bayesian analysis; maximum likelihood, large sample theory. Prerequisite: 116.
http://statweb.stanford.edu/~sabatti/Stat200/index.html
Terms: Aut, Win

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Mohanty, P. (PI)
;
Romano, J. (PI)
;
Sabatti, C. (PI)
...
more instructors for STATS 200 »
Instructors:
Mohanty, P. (PI)
;
Romano, J. (PI)
;
Sabatti, C. (PI)
;
Bhattacharya, S. (TA)
;
Bi, N. (TA)
;
Ghosh, S. (TA)
;
Gupta, S. (TA)
;
Hamidi, N. (TA)
;
Hwang, J. (TA)
;
Li, S. (TA)
;
Misiakiewicz, T. (TA)
;
Ren, Z. (TA)
;
Roquero Gimenez, J. (TA)
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)
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