Data Science W203

Statistics for Data Science

3 units

Course Description

The goal of this course is to provide students with an introduction to many different types of quantitative research methods and statistical techniques for analyzing data. We begin with a focus on measurement, inferential statistics and causal inference using the open-source statistics language, R. Topics in quantitative techniques include: descriptive and inferential statistics, sampling, experimental design, tests of difference, ordinary least squares regression, general linear models.

(Prior to Fall 2016, the course was titled “Exploring and Analyzing Data.”)

Course must be taken for a letter grade to fulfill degree requirements.

Prerequisites

Master of Information and Data Science students only. Intermediate competency in calculus is required. A college-level linear algebra course is recommended.

Course History

Spring 2018

Instructor(s): Eric Penner
Instructor(s): Eric Penner
Instructor(s): Eric Penner
Instructor(s): Ryan Kappedal
Instructor(s): Jennifer Shin
Instructor(s): Micah Gell-Redman

Fall 2017

Instructor(s): Jennifer Shin
Instructor(s): Jeff Yau
Instructor(s): Ryan Kappedal
Instructor(s): Eric Penner
Instructor(s): Eric Penner
Instructor(s): Jennifer Shin

Summer 2017

Instructor(s): Jennifer Shin
Instructor(s): Jeff Yau
Instructor(s): Jeff Yau

Last updated:

January 13, 2017