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STAA 577 - Statistical Learning and Data Mining

  • 2 credits
View available sections

Regularization, prediction, regression, classification and clustering. Students will learn to implement modern statistical techniques for analyzing the types of data that would be encountered by statisticians working in business, medicine, science and government.

Note: R programming skills (CSSA) are expected.


STAA 551 (Regression Models and Applications) or concurrent registration; STAA 561 (Probability with Applications)

Important Information

Tuition includes access to lecture recordings which are available by streamed video. Lecture recordings may also be available by download or on DVD. To determine viewing options, contact the Department of Statistics degree program staff at Visit the Department of Statistics website to learn more about what to do after registration, including creating your eID (if necessary) and accessing your course.

Textbooks and Materials

Section 801


  • An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
    James, G., Witten, D., Hastie, T., & Tibshirani, R.
    ISBN: 978-1461471370

Textbooks and materials can be purchased at the CSU Bookstore unless otherwise indicated.

R software is used.