# StanfordOnline: Statistical Learning with R

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Learn some of the main tools used in statistical modeling and data science. We cover both traditional as well as exciting new methods, and how to use them in R. Course material updated in 2021 for second edition of the course textbook.

11 weeks
3–5 hours per week
Self-paced
Free

### There is one session available:

90,202 already enrolled! After a course session ends, it will be archivedOpens in a new tab.
Starts Feb 22
Ends Aug 26

This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines; neural networks and deep learning; survival models; multiple testing. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical).

This is not a math-heavy class, so we try and describe the methods without heavy reliance on formulas and complex mathematics. We focus on what we consider to be the important elements of modern data science. Computing is done in R. There are lectures devoted to R, giving tutorials from the ground up, and progressing with more detailed sessions that implement the techniques in each chapter. We also offer a separate version of the course called Statistical Learning with Python – the chapter lectures are the same, but the lab lectures and computing are done using Python.

The lectures cover all the material in An Introduction to Statistical Learning, with Applications in R (second addition) by James, Witten, Hastie and Tibshirani (Springer, 2021). The pdf for this book is available for free on the book website.

### At a glance

• Language: English
• Video Transcript: English
• Associated skills:Random Forest Algorithm, Logistic Regression, Linear Discriminant Analysis, Data Science, Polynomial Regression, Statistical Modeling, R (Programming Language), K-Means Clustering, Data Analysis, Python (Programming Language), Bootstrap (Front-End Framework), Lasso (Programming Language), Unsupervised Learning, Supervised Learning, Artificial Neural Networks, Support Vector Machine, Principal Component Analysis, Lecturing, Statistical Learning Theory, Boosting, Statistics, Deep Learning

# What you'll learn

Skip What you'll learn
• Overview of statistical learning
• Linear regression
• Classification
• Resampling methods
• Linear model selection and regularization
• Moving beyond linearity
• Tree-based methods
• Support vector machines
• Deep learning
• Survival modeling
• Unsupervised learning
• Multiple testing

Do I need to buy a textbook?

The book is nice to own, and is available on Amazon and other booksellers.. However a free online version of An Introduction to Statistical Learning, with Applications in R (second edition) by James, Witten, Hastie and Tibshirani (Springer, 2021) is available from that website. Springer has agreed to this, so no need to worry about copyright. Of course you may not distribute printed versions of that pdf file.