undergraduate course listing @ the bren school of information and computer sciences

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Course Listing for 2016-17

Note: This is a TENTATIVE schedule. The course listings shown here are neither guaranteed, nor considered "final". Department Chairs may provide updated information regarding course offerings or faculty assignments throughout the year. Be sure to check this list regularly for new or revised information.

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Course Title description Fall 2016 Winter 2017 Spring 2017 Summer 2017

STATS 005
STATS 5
An introduction to the field of Data Science; intended for entering freshman and transfers. Padhraic Smyth

STATS 007
STATS 7
Lecture, three hours; discussion, one to two hours. Basic inferential statistics including confidence intervals and hypothesis testing on means and proportions, t-distribution, Chi Square, regression and correlation. F-distribution and nonparametric statistics included if time permits. Only one course from Statistics 7, Statistics 8, Management 7, or Biological Sciences 7 may be taken for credit. No credit for Statistics 7 if taken after Statistics 67. Brigitte Baldi (2)
Brigitte Baldi (2)
TBD
Brigitte Baldi (2)
TBD

STATS 008
STATS 8
Lecture, three hours; discussion, one hour. Teaches introductory statistical techniques used to collect and analyze experimental and observational data for health sciences and molecular, cellular, environmental and evolutionary biology. Specific topics include exploration of data producing data, probability and sampling distributions, basic statistical inference for means, proportions, linear regression, and analysis of variance. Only one course from Statistics 8, Statistics 7, Management 7, Biological Sciences 7, or Social Ecology 13 may be taken for credit. Brigitte Baldi
Brigitte Baldi
Brigitte Baldi

STATS 067
STATS 67
Lecture, three hours; discussion, two hours. Introduction to the basic concepts of probability and statistics with discussion of applications to computer science. Sevan Gregory Gulesserian
Sevan Gregory Gulesserian (2)
Harold E Dyck
TBD

STATS 068
STATS 68
Introduces key concepts in statistical computing. Techniques such as exploratory data analysis, data visualization, simulation, and optimization methods, will be presented in the context of data analysis within a statistical computing environment. Yaming Yu

Course	Title	description	Fall 2016	Winter 2017	Spring 2017

STATS 005	STATS 5	An introduction to the field of Data Science; intended for entering freshman and transfers.		Padhraic Smyth
STATS 007	STATS 7	Lecture, three hours; discussion, one to two hours. Basic inferential statistics including confidence intervals and hypothesis testing on means and proportions, t-distribution, Chi Square, regression and correlation. F-distribution and nonparametric statistics included if time permits. Only one course from Statistics 7, Statistics 8, Management 7, or Biological Sciences 7 may be taken for credit. No credit for Statistics 7 if taken after Statistics 67.	Brigitte Baldi (2)	Brigitte Baldi (2) TBD	Brigitte Baldi (2) TBD
STATS 008	STATS 8	Lecture, three hours; discussion, one hour. Teaches introductory statistical techniques used to collect and analyze experimental and observational data for health sciences and molecular, cellular, environmental and evolutionary biology. Specific topics include exploration of data producing data, probability and sampling distributions, basic statistical inference for means, proportions, linear regression, and analysis of variance. Only one course from Statistics 8, Statistics 7, Management 7, Biological Sciences 7, or Social Ecology 13 may be taken for credit.	Brigitte Baldi	Brigitte Baldi	Brigitte Baldi
STATS 067	STATS 67	Lecture, three hours; discussion, two hours. Introduction to the basic concepts of probability and statistics with discussion of applications to computer science.	Sevan Gregory Gulesserian	Sevan Gregory Gulesserian (2) Harold E Dyck	TBD
STATS 068	STATS 68	Introduces key concepts in statistical computing. Techniques such as exploratory data analysis, data visualization, simulation, and optimization methods, will be presented in the context of data analysis within a statistical computing environment.			Yaming Yu

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