--- product_id: 1594976 title: "An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)" price: "S/.602" currency: PEN in_stock: true reviews_count: 13 url: https://www.desertcart.pe/products/1594976-an-introduction-to-statistical-learning-with-applications-in-r-springer store_origin: PE region: Peru --- # An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) **Price:** S/.602 **Availability:** ✅ In Stock ## Quick Answers - **What is this?** An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) - **How much does it cost?** S/.602 with free shipping - **Is it available?** Yes, in stock and ready to ship - **Where can I buy it?** [www.desertcart.pe](https://www.desertcart.pe/products/1594976-an-introduction-to-statistical-learning-with-applications-in-r-springer) ## Best For - Customers looking for quality international products ## Why This Product - Free international shipping included - Worldwide delivery with tracking - 15-day hassle-free returns ## Description An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform. Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra. Review: This book is more like a ticket of a beautiful park - This is a wonderful book for an intro to the world of statistical learning. As an engineering students, it is very approachable and readable. It took me 2 days to finish all chapters, without exercise. To read through the chapters, it's much more enjoyable than reading other math/stat books, since the ideas behind each model or algorithms are very clear even intuitive, a lot of well-made plots make the understanding even easier. I would like to recommend to anyone who want to enter the world of statistical learning. However, from a graduate level student, I would say this book is more suitable for a undergrad stat or related field student, practitioners, or an entry level graduate student who is not majoring in stat or math. The ideas are much more intuitive than rigorous. If only use such book to do any real world problem, even though they talk about cross validation or something a little bit involved, practitioners may either came across so much problems in statistical analysis, or come to a wrong conclusion. Not saying the methods within this book is wrong, but without deep understanding of some theories or rigorous assumpions of the methods, pure blind trying different algorithms to find lowest MSE may not be suitable for some cases. Still, this is a wonderful book for two cases: 1. If you have some background in theoretical or mathematical statistics and want to gain some knowledge of applied methods, this book will be wonderful for you to find applications with your theoretical knowledge; 2. If you have few knowledge about rigorous statistics, but want to enter the world of statistical/machine learning, this one is very suitable to trigger your interest for reading deeper and more rigorous books, such as ESL. For myself, this books is more like a ticket. I have the ticket of a beautiful state park. I use it to cross the gate of the park, but stand near the gate to give an overlook of the beautiful scenes of the park. The map described on the ticket is only contained the main road of the park. If you want to check more beautiful scenes, you need more work, more tickets, more tools to take an adventure within this park for quite a while. 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. ## Features - This book presents some of the most important modeling and prediction techniques, along with relevant applications - Topics include linear regression, classification, re-sampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. - Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform ## Technical Specifications | Specification | Value | |---------------|-------| | Best Sellers Rank | #559,636 in Books ( See Top 100 in Books ) #220 in Statistics (Books) #311 in Probability & Statistics (Books) | | Customer Reviews | 4.8 out of 5 stars 1,935 Reviews | ## Images ![An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) - Image 1](https://m.media-amazon.com/images/I/61Lvnv9+CML.jpg) ## Customer Reviews ### ⭐⭐⭐⭐⭐ This book is more like a ticket of a beautiful park *by J***G on January 24, 2018* This is a wonderful book for an intro to the world of statistical learning. As an engineering students, it is very approachable and readable. It took me 2 days to finish all chapters, without exercise. To read through the chapters, it's much more enjoyable than reading other math/stat books, since the ideas behind each model or algorithms are very clear even intuitive, a lot of well-made plots make the understanding even easier. I would like to recommend to anyone who want to enter the world of statistical learning. However, from a graduate level student, I would say this book is more suitable for a undergrad stat or related field student, practitioners, or an entry level graduate student who is not majoring in stat or math. The ideas are much more intuitive than rigorous. If only use such book to do any real world problem, even though they talk about cross validation or something a little bit involved, practitioners may either came across so much problems in statistical analysis, or come to a wrong conclusion. Not saying the methods within this book is wrong, but without deep understanding of some theories or rigorous assumpions of the methods, pure blind trying different algorithms to find lowest MSE may not be suitable for some cases. Still, this is a wonderful book for two cases: 1. If you have some background in theoretical or mathematical statistics and want to gain some knowledge of applied methods, this book will be wonderful for you to find applications with your theoretical knowledge; 2. If you have few knowledge about rigorous statistics, but want to enter the world of statistical/machine learning, this one is very suitable to trigger your interest for reading deeper and more rigorous books, such as ESL. For myself, this books is more like a ticket. I have the ticket of a beautiful state park. I use it to cross the gate of the park, but stand near the gate to give an overlook of the beautiful scenes of the park. The map described on the ticket is only contained the main road of the park. If you want to check more beautiful scenes, you need more work, more tickets, more tools to take an adventure within this park for quite a while. ### ⭐⭐⭐⭐⭐ 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. ## 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) --- ## Why Shop on Desertcart? - 🛒 **Trusted by 1.3+ Million Shoppers** — Serving international shoppers since 2016 - 🌍 **Shop Globally** — Access 737+ million products across 21 categories - 💰 **No Hidden Fees** — All customs, duties, and taxes included in the price - 🔄 **15-Day Free Returns** — Hassle-free returns (30 days for PRO members) - 🔒 **Secure Payments** — Trusted payment options with buyer protection - ⭐ **TrustPilot Rated 4.5/5** — Based on 8,000+ happy customer reviews **Shop now:** [https://www.desertcart.pe/products/1594976-an-introduction-to-statistical-learning-with-applications-in-r-springer](https://www.desertcart.pe/products/1594976-an-introduction-to-statistical-learning-with-applications-in-r-springer) --- *Product available on Desertcart Peru* *Store origin: PE* *Last updated: 2026-05-24*