## STATS 48N: Riding the Data Wave

Imagine collecting a bit of your saliva and sending it in to one of the personalized genomics company: for very little money you will get back information about hundreds of thousands of variable sites in your genome. Records of exposure to a variety of chemicals in the areas you have lived are only a few clicks away on the web; as are thousands of studies and informal reports on the effects of different diets, to which you can compare your own. What does this all mean for you? Never before in history humans have recorded so much information about themselves and the world that surrounds them. Nor has this data been so readily available to the lay person. Expression as "data deluge'' are used to describe such wealth as well as the loss of proper bearings that it often generates. How to summarize all this information in a useful way? How to boil down millions of numbers to just a meaningful few? How to convey the gist of the story in a picture without misleading oversimplifications? To answer these questions we need to consider the use of the data, appreciate the diversity that they represent, and understand how people instinctively interpret numbers and pictures. During each week, we will consider a different data set to be summarized with a different goal. We will review analysis of similar problems carried out in the past and explore if and how the same tools can be useful today. We will pay attention to contemporary media (newspapers, blogs, etc.) to identify settings similar to the ones we are examining and critique the displays and summaries there documented. Taking an experimental approach, we will evaluate the effectiveness of different data summaries in conveying the desired information by testing them on subsets of the enrolled students.

Terms: Aut
| Units: 3
| UG Reqs: WAY-AQR, WAY-FR

Instructors:
Sabatti, C. (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, t-tests, correlation, and regression. Possible topics: analysis of variance and chi-square tests, computer statistical packages.

Terms: Aut, Win, Spr, Sum
| Units: 5
| UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR

Instructors:
DiCiccio, C. (PI)
;
Taylor, J. (PI)
;
Thomas, E. (PI)
...
more instructors for STATS 60 »

Instructors:
DiCiccio, C. (PI)
;
Taylor, J. (PI)
;
Thomas, E. (PI)
;
Walther, G. (PI)
;
Xia, L. (PI)
;
Chin, A. (TA)
;
DiCiccio, C. (TA)
;
Donnat, C. (TA)
;
Janson, L. (TA)
;
Miao, J. (TA)
;
Orenstein, P. (TA)
;
Patterson, E. (TA)
;
SUR, P. (TA)
;
YAN, J. (TA)
;
YANG, J. (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: 4-5
| UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR

Instructors:
Rajaratnam, B. (PI)
;
Wang, J. (PI)
;
Zheng, C. (PI)
...
more instructors for STATS 110 »

Instructors:
Rajaratnam, B. (PI)
;
Wang, J. (PI)
;
Zheng, C. (PI)
;
Bhattacharya, B. (TA)
;
Bi, N. (TA)
;
Friedberg, R. (TA)
;
Panigrahi, S. (TA)

## 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: 3-5
| UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR

Instructors:
Khare, A. (PI)
;
Wang, R. (PI)
;
Xia, L. (PI)
;
Gorham, J. (SI)
;
Huang, R. (SI)
;
Le, Y. (SI)
;
Tsao, A. (SI)
;
Choi, Y. (TA)
;
Huang, R. (TA)
;
Le, Y. (TA)
;
Sepehri, A. (TA)
;
Tian, X. (TA)

## 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: 3-5
| UG Reqs: GER:DB-Math, WAY-AQR

Instructors:
Mukherjee, R. (PI)
;
Basu, K. (TA)
;
Fukuyama, J. (TA)
...
more instructors for STATS 141 »

## 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 micro-arrays 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

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, t-tests, correlation, and regression. Possible topics: analysis of variance and chi-square tests, computer statistical packages.

Terms: Aut, Win, Spr, Sum
| Units: 5

Instructors:
DiCiccio, C. (PI)
;
Taylor, J. (PI)
;
Thomas, E. (PI)
...
more instructors for STATS 160 »

Instructors:
DiCiccio, C. (PI)
;
Taylor, J. (PI)
;
Thomas, E. (PI)
;
Walther, G. (PI)
;
Xia, L. (PI)
;
DiCiccio, C. (TA)
;
Donnat, C. (TA)
;
Miao, J. (TA)
;
Orenstein, P. (TA)
;
SUR, P. (TA)
;
YAN, J. (TA)
;
YANG, J. (TA)

## STATS 195: Introduction to R (CME 195)

This short course runs for the first four weeks 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 application-driven. 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

Instructors:
Michael, H. (PI)
;
Suo, X. (PI)

## STATS 198: Practical Training

For students majoring in Mathematical and Computational Science only. Students obtain employment in a relevant industrial or research activity to enhance their professional experience. Students may enroll in summer quarters only for a total of three times. For corresponding Statistics master's course see
Stats 298.

Terms: Aut, Win, Spr, Sum
| Units: 1
| Repeatable for credit

Instructors:
Duchi, J. (PI)
;
Efron, B. (PI)
;
Holmes, S. (PI)
;
Ioannidis, J. (PI)
;
Sabatti, C. (PI)
;
Siegmund, D. (PI)
;
Taylor, J. (PI)

## STATS 199: Independent Study

For undergraduates.

Terms: Aut, Win, Spr, Sum
| Units: 1-15
| Repeatable for credit

Instructors:
Candes, E. (PI)
;
Dembo, A. (PI)
;
Diaconis, P. (PI)
...
more instructors for STATS 199 »

Instructors:
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)
;
Olkin, I. (PI)
;
Olshen, R. (PI)
;
Owen, A. (PI)
;
Rajaratnam, B. (PI)
;
Rogosa, D. (PI)
;
Romano, J. (PI)
;
Siegmund, D. (PI)
;
Switzer, P. (PI)
;
Taylor, J. (PI)
;
Tibshirani, R. (PI)
;
Walther, G. (PI)
;
Wong, W. (PI)
;
Zhang, N. (PI)