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.
This course requires proctored exams. Details will be provided in the course syllabus.
This course can be applied toward:
STAA 551 (Regression Models and Applications); STAA 561 (Probability with Applications). This is a partial semester course.
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 email@example.com. 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
Textbooks and materials can be purchased at the CSU Bookstore unless otherwise indicated.
- An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
James, G., Witten, D., Hastie, T., & Tibshirani, R.
R software is used.