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This introductory-level course in supervised learning covers regression and classification techniques, including linear and polynomial regression, logistic regression, cross-validation, and more. It also touches on some unsupervised learning methods like principal components and clustering. The course emphasizes practical understanding without heavy math, using R for computing and providing detailed tutorials on its usage. Explore modern data analysis essentials with us.

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 analysis. 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 by James, Witten, Hastie and Tibshirani, 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.

Prerequisites: Foundational knowledge in statistics, linear algebra, and computing is recommended.

Instructors: Learn from Trevor Hastie, Professor of Statistics at Stanford University, and Robert Tibshirani, Professor in Health Research and Policy and Statistics at Stanford University.

This course delves into the fundamentals of R, a popular programming language and software environment extensively utilized by data analysts, statisticians, and data scientists for statistical computing and graphics across the globe. It provides an introductory journey into R, encompassing installation procedures and foundational statistical operations. The course equips you with the skills to handle variables and external datasets, create functions, and offers insights from one of the R language’s co-creators, Robert Gentleman. Explore the world of R and enhance your statistical computing capabilities with this comprehensive course.

This course equips you with core competencies in R, covering data types, variables, vectors, matrices, lists, data frames, data import, logical statements, loops, functions, data plotting, visualization, and basic statistical functions. Prior experience in a scientific or engineering discipline is helpful but not mandatory, making it accessible to learners with basic computer skills. Join us to enhance your proficiency in R and excel in statistical computing and data analysis.