---
product_id: 452226048
title: "An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)"
price: "€ 92.09"
currency: EUR
in_stock: true
reviews_count: 13
url: https://www.desertcart.fr/products/452226048-an-introduction-to-statistical-learning-with-applications-in-r-springer
store_origin: FR
region: France
---

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

**Price:** € 92.09
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- **What is this?** An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)
- **How much does it cost?** € 92.09 with free shipping
- **Is it available?** Yes, in stock and ready to ship
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## Description

desertcart.com: An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics): 9781071614204: James, Gareth, Witten, Daniela, Hastie, Trevor, Tibshirani, Robert: Books

Review: The Most Accessible Statistics Textbook - The authors Hastie and Tibshirani are legends in the stats world, creating GAM and LASSO respectively. Their other textbook "The Elements of Statistical Learning" is geared for PhD students. This textbook is very accessible, with figures and lots of sample code. The target audience is any aspiring data scientist who can learn to code and wants to actually understand what the code/models are doing (but doesn't need to be able to derive all the original math by hand). In addition to teaching different analyses, this book does a great job on explaining key statistical analysis concepts, like bias vs variance tradeoff, k-fold cross-validation, bootstrapping, finding the right balance in model complexity for your dataset, etc. There is both an R and a Python edition. The 2nd edition includes 3 new chapters on survival analysis, multiple testing, and neural nets. There is a free Stanford MOOC that uses this text.
Review: Best introduction book on the subject - I would like to upskill data related subject included introduction to statistics. Although I reached the beginning of chapter 3 but I've read and worked through all problems and exercises. Those are valuable although there are no solutions. I let gou recap and know in and out of R. But yes, you have to effort into it. Printings are in color. Paper is in good quality. Although thee cover slightly hit and bumped down a bit on the top left, I dont mind that much as it is a hard cover which protects paper inside. I believe after finish this one, I will continue the next in the series. In short, good purchase.

## Technical Specifications

| Specification | Value |
|---------------|-------|
| Best Sellers Rank | #101,799 in Books ( See Top 100 in Books ) #4 in Mathematical & Statistical Software #24 in Probability & Statistics (Books) #124 in Artificial Intelligence & Semantics |
| Customer Reviews | 4.7 4.7 out of 5 stars (437) |
| Dimensions  | 6 x 1.25 x 9 inches |
| Edition  | Second Edition 2021 |
| ISBN-10  | 1071614207 |
| ISBN-13  | 978-1071614204 |
| Item Weight  | 7.6 ounces |
| Language  | English |
| Part of series  | Springer Texts in Statistics |
| Print length  | 622 pages |
| Publication date  | July 30, 2022 |
| Publisher  | Springer |

## Images

![An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) - Image 1](https://m.media-amazon.com/images/I/61YuCWZkUcL.jpg)

## Customer Reviews

### ⭐⭐⭐⭐⭐ The Most Accessible Statistics Textbook
*by M***Z on January 30, 2024*

The authors Hastie and Tibshirani are legends in the stats world, creating GAM and LASSO respectively. Their other textbook "The Elements of Statistical Learning" is geared for PhD students. This textbook is very accessible, with figures and lots of sample code. The target audience is any aspiring data scientist who can learn to code and wants to actually understand what the code/models are doing (but doesn't need to be able to derive all the original math by hand). In addition to teaching different analyses, this book does a great job on explaining key statistical analysis concepts, like bias vs variance tradeoff, k-fold cross-validation, bootstrapping, finding the right balance in model complexity for your dataset, etc. There is both an R and a Python edition. The 2nd edition includes 3 new chapters on survival analysis, multiple testing, and neural nets. There is a free Stanford MOOC that uses this text.

### ⭐⭐⭐⭐⭐ Best introduction book on the subject
*by B***B on October 9, 2024*

I would like to upskill data related subject included introduction to statistics. Although I reached the beginning of chapter 3 but I've read and worked through all problems and exercises. Those are valuable although there are no solutions. I let gou recap and know in and out of R. But yes, you have to effort into it. Printings are in color. Paper is in good quality. Although thee cover slightly hit and bumped down a bit on the top left, I dont mind that much as it is a hard cover which protects paper inside. I believe after finish this one, I will continue the next in the series. In short, good purchase.

### ⭐⭐⭐⭐⭐ Must read for data analysis
*by K***L on August 2, 2025*

A true book for data analytics student.

## 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)
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

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*Product available on Desertcart France*
*Store origin: FR*
*Last updated: 2026-03-21*