---
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title: "An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)"
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# An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)

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desertcart.com: An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics): 9781461471370: James, Gareth: Books

Review: Written by statisticians for non-statisticians - Without any suspense, "An Introduction to Statistical Learning" (ISL) by James, Witten, Hastie and Tibshirani is a key book in the Data Science literature. I would summarize it as a book written by statisticians for non-statisticians. Indeed, while the book "The Elements of Statistical Learning" was heavy on theory and equations, ISL is the practical counterpart. The book is very clear and contains only theory you need to understand the data mining algorithms covered. It's thus a invaluable resource for Data Scientists who don't need all theorems and proofs related to a given algorithm, but still need to understand how it works. Several examples are provided to illustrate each algorithm. Each chapter contains a section with R labs, showing the code needed to move from reading the book to doing data science. The book has a strong emphasis on linear regression and related non-linear approaches (more than half of the book). This lets very few place to other approaches such as decision trees and SVM, which are still covered. The final chapter rapidly covers PCA and clustering. Although the book is targeted towards a larger audience than statisticians, you shouldn't be afraid of equations (by the way, if you look for an excellent book covering data science algorithms with nearly no equation, have a look at "Data Science for Business" from Provost and Fawcett). With such an excellent book, we are obviously more exigent and I was looking for more coverage of validity indices for clustering, Support Vector Regression, and a final chapter about trends and challenges. In conclusion, ISL is the definitive resource for Data Scientists who want to get the correct level of statistical background in their work.
Review: cover all of your bases - If you want to build a comprehensive machine learning library, this would be the first book to purchase. While it does cover all of the basics, it is not watered down by any means. (I had the same fear as BK Reader) I found the following to be especially helpful; 1. Straight talk - These experts come right and say which methods work best under which circumstances. While there are many fancy algorithms covered in the book, they highlight the advantages of the simpler ones. 2. Emphasis on subjects that are not heavily addressed in most ML books - They thoroughly cover the challenges of high-dimensionality, data cleaning, and standardization. They do not limit their attention to these subjects to one chapter. They bring them up continually throughout the book. 3. Expertise - Dr. Hastie and Dr. Tibshirani are two of the thought leaders in statistical learning. You can be assured that you are learning from the best. 4. Many levels of depth - While the book does cover the basics, it is not watered down by any means. (I had the same worry as BK Reader) There is a great deal for any student of statistics; beginner or advanced. 5. R code - You are given enough code and examples to gain confidence in your ability to independently perform excellent analysis and modeling. 6. The concepts are just plain exciting! - You will feel an excitement as you discover and re-discover the algorithms they present. The book is a standard work along with Elements of Statistical Learning and Pattern Recognition and Machine Learning (the Bayesian approach). If you enjoy the book, you may also want to consider Applied Predictive Modeling. It has the same style and approach.

## Technical Specifications

| Specification | Value |
|---------------|-------|
| Best Sellers Rank | #308,934 in Books ( See Top 100 in Books ) #75 in Statistics (Books) #257 in Probability & Statistics (Books) |
| Customer Reviews | 4.7 4.7 out of 5 stars (1,931) |
| Dimensions  | 6.1 x 1 x 9.3 inches |
| Edition  | 1st |
| ISBN-10  | 1461471370 |
| ISBN-13  | 978-1461471370 |
| Item Weight  | 2.31 pounds |
| Language  | English |
| Part of series  | Springer Texts in Statistics |
| Print length  | 426 pages |
| Publication date  | January 1, 2013 |
| Publisher  | SPRINGER |

## Images

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## Customer Reviews

### ⭐⭐⭐⭐⭐ Written by statisticians for non-statisticians
*by S***A on May 12, 2016*

Without any suspense, "An Introduction to Statistical Learning" (ISL) by James, Witten, Hastie and Tibshirani is a key book in the Data Science literature. I would summarize it as a book written by statisticians for non-statisticians. Indeed, while the book "The Elements of Statistical Learning" was heavy on theory and equations, ISL is the practical counterpart. The book is very clear and contains only theory you need to understand the data mining algorithms covered. It's thus a invaluable resource for Data Scientists who don't need all theorems and proofs related to a given algorithm, but still need to understand how it works. Several examples are provided to illustrate each algorithm. Each chapter contains a section with R labs, showing the code needed to move from reading the book to doing data science. The book has a strong emphasis on linear regression and related non-linear approaches (more than half of the book). This lets very few place to other approaches such as decision trees and SVM, which are still covered. The final chapter rapidly covers PCA and clustering. Although the book is targeted towards a larger audience than statisticians, you shouldn't be afraid of equations (by the way, if you look for an excellent book covering data science algorithms with nearly no equation, have a look at "Data Science for Business" from Provost and Fawcett). With such an excellent book, we are obviously more exigent and I was looking for more coverage of validity indices for clustering, Support Vector Regression, and a final chapter about trends and challenges. In conclusion, ISL is the definitive resource for Data Scientists who want to get the correct level of statistical background in their work.

### ⭐⭐⭐⭐⭐ cover all of your bases
*by J***N on January 26, 2014*

If you want to build a comprehensive machine learning library, this would be the first book to purchase. While it does cover all of the basics, it is not watered down by any means. (I had the same fear as BK Reader) I found the following to be especially helpful; 1. Straight talk - These experts come right and say which methods work best under which circumstances. While there are many fancy algorithms covered in the book, they highlight the advantages of the simpler ones. 2. Emphasis on subjects that are not heavily addressed in most ML books - They thoroughly cover the challenges of high-dimensionality, data cleaning, and standardization. They do not limit their attention to these subjects to one chapter. They bring them up continually throughout the book. 3. Expertise - Dr. Hastie and Dr. Tibshirani are two of the thought leaders in statistical learning. You can be assured that you are learning from the best. 4. Many levels of depth - While the book does cover the basics, it is not watered down by any means. (I had the same worry as BK Reader) There is a great deal for any student of statistics; beginner or advanced. 5. R code - You are given enough code and examples to gain confidence in your ability to independently perform excellent analysis and modeling. 6. The concepts are just plain exciting! - You will feel an excitement as you discover and re-discover the algorithms they present. The book is a standard work along with Elements of Statistical Learning and Pattern Recognition and Machine Learning (the Bayesian approach). If you enjoy the book, you may also want to consider Applied Predictive Modeling. It has the same style and approach.

### ⭐⭐⭐⭐⭐ Excellent Practical Introduction to Learning
*by M***S on October 24, 2013*

The book provides the right amount of theory and practice, unlike the earlier (venerable and, by now, stable) text authored (partly) by the last two authors of this one (Elements of Statistical Learning), which was/is a little heavy on the theoretical side (at least for practitioners without a strong mathematical background). The authors make no pretense about this either. The Preface says "But ESL is intended for individuals with advanced training in the mathematical sciences. An Introduction to Statistical Learning (ISL) arose from the perceived need for a broader and less technical treatment of these topics." ISL is neither as comprehensive nor as in-depth as ESL. It is, however, an excellent introduction to Learning due to the ability of the authors to strike a perfect balance between theory and practice. Theory is there to aim the reader as to understand the purpose and the "R Labs" at the end of each chapter are as valuable (or perhaps even more) than the end-of-chapter exercises. ISL is an excellent choice for a two-semester advanced undergraduate (or early graduate) course, practitioners trained in classical statistics who want to enter the Learning space, and seasoned Machine Learners. It is especially helpful for getting the fundamentals down without being bogged down in heavy mathematical theory, a great way to kick-off corporate Learning units, or as an aid to help statisticians and learners communicate better. A needed and welcome addition to the Learning literature, authored by some of the most well respected names in industry and academia. A classic in the making. Recommended unreservedly. ____________________________________________ UPDATE (12/17/2013): Two of the authors (Hastie & Tibshirani) are offering a 10-week free online course (StatLearning: Statistical Learning) based on this book found at Stanford University's Web site (Starting Jan. 21, 2014). They also say that "As of January 5, 2014, the pdf for this book will be available for free, with the consent of the publisher, on the book website." Amazing opportunity! Enjoy! ____________________________________________ UPDATE (04/03/2014): I took the course above and found it very helpful and insightful. You don't need the course to understand the book. If anything, the course videos are less detailed than the book. It is certainly nice, though, to see the actual authors explain the material. Also, the interviews by Efron and Friedman were a nice touch. The course will be offered again in the future.

## Frequently Bought Together

- An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)

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