## 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:
Tran, L. (PI)
;
Walther, G. (PI)
;
Bhattacharya, S. (TA)
...
more instructors for STATS 202 »

Instructors:
Tran, L. (PI)
;
Walther, G. (PI)
;
Bhattacharya, S. (TA)
;
Ghosh, S. (TA)
;
Gibbs, I. (TA)
;
Jang, J. (TA)
;
Liu, S. (TA)
;
Rajanala, S. (TA)
;
Wang, X. (TA)
;
Wang, Y. (TA)
;
Wu, H. (TA)
;
Zhong, C. (TA)
;
Zhou, K. (TA)

## STATS 203: Introduction to Regression Models and Analysis of Variance

Modeling and interpretation of observational and experimental data using linear and nonlinear regression methods. Model building and selection methods. Multivariable analysis. Fixed and random effects models. Experimental design. Prerequisites: a post-calculus introductory probability course, e.g.
STATS 116. In addition, a co-requisite post-calculus mathematical statistics course, e.g.
STATS 200, basic computer programming knowledge, and some familiarity with matrix algebra.

Terms: Win
| Units: 3

## STATS 203V: Introduction to Regression Models and Analysis of Variance

Modeling and interpretation of observational and experimental data using linear and nonlinear regression methods. Model building and selection methods. Multivariable analysis. Fixed and random effects models. Experimental design. This course is offered remotely only via video segments (MOOC style). TAs will host remote weekly office hours using an online platform such as Zoom. Prerequisites: a post-calculus introductory probability course, e.g.
STATS 116. In addition, a co-requisite post-calculus mathematical statistics course, e.g.
STATS 200, basic computer programming knowledge, and some familiarity with matrix algebra.

Terms: Sum
| Units: 3

## STATS 204: Sampling

How best to take data and where to sample it. Examples include surveys and sampling from data warehouses. Emphasis is on methods for finite populations. Topics: simple random sampling, stratified sampling, cluster sampling, ratio and regression estimators, two stage sampling.

Terms: Spr
| Units: 3

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

## STATS 211: Meta-research: Appraising Research Findings, Bias, and Meta-analysis (CHPR 206, HRP 206, MED 206)

Open to graduate, medical, and undergraduate students. Appraisal of the quality and credibility of research findings; evaluation of sources of bias. Meta-analysis as a quantitative (statistical) method for combining results of independent studies. Examples from medicine, epidemiology, genomics, ecology, social/behavioral sciences, education. Collaborative analyses. Project involving generation of a meta-research project or reworking and evaluation of an existing published meta-analysis. Prerequisite: knowledge of basic statistics.

Terms: Win
| Units: 3

Instructors:
Ioannidis, J. (PI)
;
Jansen, J. (SI)

## STATS 290: Computing for Data Science

Programming and computing techniques for the requirements of data science: acquisition and organization of data; visualization, modelling and inference for scientific applications; presentation and interactive communication of results. Emphasis on computing for substantial projects. Software development with emphasis on R, plus other key software tools. Prerequisites: Programming experience including familiarity with R; computing at least at the level of
CS 106; statistics at the level of
STATS 110 or 141.

Terms: Win
| Units: 3

Instructors:
Chambers, J. (PI)
;
Narasimhan, B. (PI)
;
Bates, S. (TA)
...
more instructors for STATS 290 »

Instructors:
Chambers, J. (PI)
;
Narasimhan, B. (PI)
;
Bates, S. (TA)
;
Fry, K. (TA)
;
Gibbs, I. (TA)

Filter Results: