STAT 6021

Linear Models for Data Science

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Course Description

Pre-Requisite(s): 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.


  • Prince Afriyie

     Rating

     Difficulty

     GPA

     Sections

    Last Taught

    Summer 2025

  • Jeffrey Woo

     Rating

     Difficulty

     GPA

     Sections

    Last Taught

    Spring 2025

  • Dan Spitzner

     Rating

     Difficulty

     GPA

    3.58

     Sections

    Last Taught

    Fall 2018

  • Gretchen Martinet

     Rating

    2.33

     Difficulty

    3.00

     GPA

    3.68

     Sections

    Last Taught

    Fall 2017

  • Jeffrey Holt

     Rating

     Difficulty

     GPA

    3.90

     Sections

    Last Taught

    Fall 2015