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 humanities or social 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 data analysis. 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, data transformation and visualization, simple statistical tests, 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:
Baiocchi, M. (PI)
;
Khazenzon, A. (PI)
;
Leong, Y. (PI)
...
more instructors for STATS 60 »
Instructors:
Baiocchi, M. (PI)
;
Khazenzon, A. (PI)
;
Leong, Y. (PI)
;
Poldrack, R. (PI)
;
Schwartz, J. (PI)
;
Xia, L. (PI)
;
bonnen, t. (PI)
;
ten Brink, M. (PI)
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
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:
Miolane, N. (PI)
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:
Siegmund, D. (PI)
;
Xia, L. (PI)
STATS 141: Biostatistics (BIO 141)
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 associations (linear and logistic regression); and methods for categorical data (contingency tables and odds ratio). Course content integrated with statistical computing in R.
Terms: Aut

Units: 35

UG Reqs: GER:DBMath, WAYAQR

Grading: Letter or Credit/No Credit
Instructors:
Siegmund, D. (PI)
STATS 155: Statistical Methods in Computational Genetics
The computational methods necessary for the construction and evaluation of sequence alignments and phylogenies built from molecular data and genetic data such as microarrays and data base searches. How to formulate biological problems in an algorithmic decomposed form, and building blocks common to many problems such as Markovian models, multivariate analyses. Some software covered in labs (Python, Biopython, XGobi, MrBayes, HMMER, Probe). Prerequisites: knowledge of probability equivalent to
STATS 116,
STATS 202 and one class in computing at the
CS 106 level. Writing intensive course for undergraduates only. Instructor consent required. (WIM)
Terms: Aut

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Holmes, S. (PI)
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
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)
;
Duchi, J. (PI)
;
Efron, B. (PI)
...
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Instructors:
Baiocchi, M. (PI)
;
Duchi, J. (PI)
;
Efron, B. (PI)
;
Rogosa, D. (PI)
;
Sabatti, C. (PI)
;
Taylor, J. (PI)