• Course Syllabus - course contents, rules, links, and other info
  
• Canvas - homework, solutions, grades, discussions.
  
• Main textbook (R version) - An Introduction to Statistical Learning with Applications in R by G. James, D. Witten, T. Hastie, and R. Tibshirani, 2nd edition; Springer, 2021. ISBN 1071614177.
  
• Main textbook (Python version) - An Introduction to Statistical Learning with Applications in Python by G. James, D. Witten, T. Hastie, R. Tibshirani, and J. Taylor; Springer, 2023. ISBN 3031387465.
  
• Book materials for the R version - Data sets, R codes, errata, slides, video lectures, etc.
  
• Book materials for the Python version - Data sets, slides, ISLP instructions, and manual.
  
• Supplementary textbook - The Elements of Statistical Learning: Data Mining, Inference, and Prediction, by T. Hastie, R. Tibshirani, and J. Friedman, 2nd Edition; Springer, 2009. ISBN 0387848576.

R corner

Python corner

      Classroom labs

• Lab #1 First steps in R and in Python
• Lab #2 Flexibility - regression and splines in R and in Python
• Lab #3 Regression with dummy variables and interactions in R and in Python
• Lab #4 Polynomial regression in R and in Python
• Lab #5 Partial F tests and lack of fit tests in R and in Python
• Lab #6 Regression diagnostics in R and in Python
• Lab #7 KNN in R and in Python
• Lab #8 Logistic regression in R and in Python
• Lab #9 Discriminant analysis - LDA and QDA in R and in Python
• Lab #9a Geometry of LDA and QDA in R and in Python
• Lab #10 Cross-validation in R and in Python
• Lab #11 Jackknife and Bootstrap in R and in Python
• Lab #11a Bootstrap applcation to vaccine efficacy in R and in Python
• Lab #11b Jackknife and Bootstrap for Traffic Problems in R and in Python
• Lab #12 Dimension reduction - variable selection and ridge regression in R and in Python
• Lab #13 Dimension reduction - ridge regression and LASSO in R and in Python
• Lab #14 Principal components - PCR and PLS in R and in Python
• Lab #15 Polynomial regression and splines in R
• Lab #16 Trees in R and in Python
• Lab #17 Trees-2 Bagging and Random forests in R and in Python
• Lab #18 Support vector machines in R and in Python
• Lab #19 Deep Learning in R and in Python
• Lab #20 Clustering in R and in Python
• Lab #21 Applications in R

Datasets

From our textbook

Data from our textbook
You can also get all these data in R by typing
       > install.packages("ISLR2")
       > library(ISLR2)

• All data from our textbook - zip folder, RDA format
• Their description
• Advertising data, CSV
• Auto data, CSV
• Boston data, CSV
• Caravan data, CSV
• Carseats data, CSV
• College data, CSV
• Default data, CSV
• Heart data, CSV
• Hitters data, CSV
• Orange Juice data, CSV
• Portfolio data, CSV
• Smarket (S&P Stock Market) data, CSV
• Wage data, CSV
• Weekly S&P Stock Market data, CSV

Other data

• US population data
• Depression data with their description
• Field Goals data and their description
• Home sales data (CSV)
• Titanic dataset and its description.
  Here is a more complete Titanic dataset and its description.
• Restaurants dataset and its description
• Birth weights dataset (birthwt) and its description from library MASS, Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

Data sources for the final project