## STATS 48N: Riding the Data Wave (BIODS 48N)

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 an
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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)
;
Ren, Z. (TA)

## 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:
Auelua-Toomey, S. (PI)
;
Jain, V. (PI)
;
Kong, N. (PI)
...
more instructors for STATS 60 »

Instructors:
Auelua-Toomey, S. (PI)
;
Jain, V. (PI)
;
Kong, N. (PI)
;
Poldrack, R. (PI)
;
Walters, J. (PI)
;
Walther, G. (PI)
;
Dey, A. (TA)
;
Feldman, M. (TA)
;
Harrison, M. (TA)
;
Jeong, Y. (TA)
;
Jing, A. (TA)
;
Kirshenbaum, J. (TA)
;
Xu, H. (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
| Units: 5
| UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR

## 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: 4
| UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR

Instructors:
Dubey, P. (PI)
;
Schramm, T. (PI)
;
Bhattacharya, S. (TA)
...
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Instructors:
Dubey, P. (PI)
;
Schramm, T. (PI)
;
Bhattacharya, S. (TA)
;
Gupta, S. (TA)
;
Zhou, K. (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, 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:
Auelua-Toomey, S. (PI)
;
Harrison, M. (PI)
;
Jain, V. (PI)
...
more instructors for STATS 160 »

Instructors:
Auelua-Toomey, S. (PI)
;
Harrison, M. (PI)
;
Jain, V. (PI)
;
Kirshenbaum, J. (PI)
;
Kong, N. (PI)
;
Poldrack, R. (PI)
;
Walters, J. (PI)
;
Walther, G. (PI)
;
Dey, A. (TA)
;
Feldman, M. (TA)
;
Jeong, Y. (TA)
;
Jing, A. (TA)
;
Xu, H. (TA)

## STATS 199: Independent Study

For undergraduates.

Terms: Aut, Win, Spr, Sum
| Units: 1-15
| Repeatable
for 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)
;
Lai, T. (PI)
;
Linderman, S. (PI)
;
Rogosa, D. (PI)
;
Sabatti, C. (PI)
;
Taylor, J. (PI)
;
Wager, S. (PI)
;
Walther, G. (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; Neyman-Pearson theory. Bayesian analysis; maximum likelihood, large sample theory. Prerequisite:
STATS 116.

Terms: Aut, Win
| Units: 4

Instructors:
Romano, J. (PI)
;
Sabatti, C. (PI)
;
Tay, J. (PI)
;
Gibbs, I. (TA)
;
Li, S. (TA)
;
Misiakiewicz, T. (TA)
;
Ray, S. (TA)
;
Tirlea, M. (TA)
;
Wu, Y. (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

Instructors:
Taylor, J. (PI)
;
Cherian, J. (TA)
;
Fry, K. (TA)
;
Gablenz, P. (TA)
;
Ghosh, S. (TA)
;
Jang, J. (TA)
;
MacKay, M. (TA)
;
Morrison, T. (TA)
;
Yang, J. (TA)

## STATS 207: Introduction to Time Series Analysis

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 level of 200.

Terms: Aut
| Units: 3

## STATS 209A: Topics in Causal Inference (MS&E 327)

This course introduces the fundamental ideas and methods in causal inference, and surveys a broad range of problems and applications. Emphasis will be on framing causal problems and identifying causal effects in both randomized experiments and observational studies. Topics will include: the potential outcomes framework; randomization-based inference and covariate adjustment; matching, and IPW; instrumental variables, regression discontinuity and synthetic ncontrols. Examples and applications will be taken from the fields of education, political science, economics, public health and digital marketing.

Terms: Aut
| Units: 3

Instructors:
Basse, G. (PI)
;
Han, K. (TA)

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