STAT 6021
Linear Models for Data Science
Course Description
Prerequisites
A previous statistics course, a previous linear algebra course, and permission of instructor
An introduction to linear statistical models in the context of data science. Topics include simple and multiple linear regression, generalized linear models, time series, analysis of covariance, tree-based classification, and principal components. The primary software is R.Prerequisite: A previous statistics course, a previous linear algebra course, and permission of instructor.
Instructors
Prince Afriyie
Summer 2025
motuwethfr 9:15am - 11:30am
—
Rating
—
Difficulty
—
GPA
Jeffrey Woo
Spring 2025
Tu 8:30pm - 9:30pm
—
Rating
—
Difficulty
3.75
GPA
Dan Spitzner
Fall 2018
—
Rating
—
Difficulty
3.58
GPA
Gretchen Martinet
Fall 2017
2.3
Rating
3.0
Difficulty
3.68
GPA
Jeffrey Holt
Fall 2015
—
Rating
—
Difficulty
3.90
GPA