An Introduction to Statistical Learning with Applications in R is intended for anyone who is interested in using modern statistical methods for modelling and prediction from data. This group includes scientists, engineers, data analysts, data scientists, and quants, but also less technical individuals with degrees in non-quantitative fields such as the social sciences or business.

Writers expect that the reader will have had at least one elementary course in statistics. Background in linear regression is also useful, though not required since we review the key concepts behind linear regression in Chapter 3. The mathematical level of this book is modest, and detailed knowledge of matrix operations is not required. This book provides an introduction to the statistical programming language R. Previous exposure to a programming language, such as MATLAB or Python, is useful but not required.

The first edition of this book has been used to teach masters and PhD students in business, economics, computer science, biology, earth sciences, psychology, and many other areas of the physical and social sciences. It has also been used to teach advanced undergraduates who have already taken a course on linear regression. In the context of a more mathematically rigorous course in which ESL serves as the primary textbook, ISL could be used as a supplementary text for teaching computational aspects of the various approaches.

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