Data Science 203

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.

Skill Sets

Research design / Statistical analysis

Tools

R

Current Course Designers

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Paul Laskowski
Assistant Adjunct Professor Alumni (PhD 2009)

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D. Alex Hughes
D. Alex Hughes
Assistant Adjunct Professor
305B South Hall

Original Course Designer

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

Previously listed as DATASCI W203. Prior to Fall 2016, the course was titled “Exploring and Analyzing Data.”

Prerequisites

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

Video

datascience@berkeley | Statistics For Data Science

datascience@berkeley | Statistics For Data Science

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Course Spotlight

MIDS Course Spotlight: Data Science W203 (October 5, 2020)

Course History

Spring 2023

Instructor(s): Sushovan Majhi
Instructor(s): D. Alex Hughes
Instructor(s): Mark Labovitz
Instructor(s): Mark Labovitz
Instructor(s): Tanya Roosta
Instructor(s): Gunnar Kleemann
Instructor(s): Gunnar Kleemann
Instructor(s): Gunnar Kleemann
Instructor(s): Bill Chung
Instructor(s): Sushovan Majhi

Fall 2022

Instructor(s): Gunnar Kleemann
Instructor(s): Tanya Roosta
Instructor(s): Sushovan Majhi
Instructor(s): Mark Labovitz
Instructor(s): Paul Laskowski

Last updated:

October 6, 2022