Link to Content:

PDF via Gareth James - USC.edu

Created/Published/Taught by:

Gareth James

Daniela Witten

Trevor Hastie

Robert Tibshirani

Content Found Via:

"16 Free Data Science Books" - wzchen.com

Free? Partially: Some Free Content, Some Paid

Cost Range:

$0.00 - $79.99

Tags: bagging / bayes theorem / boosting / bootstrap / classification / cross-validation / decision trees / dimension reduction / generalized additive models / k nearest neighbors (k-NN) / linear discriminant analysis / linear model selection / linear regression / logistic regression / machine learning / polynomial regression / principal components analysis / R / random forests / regularization / ROC curves / splines / statistical learning / supervised learning / support vector machines / unsupervised learning

Difficulty Rating:

from Amazon:

“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.”

Note: original source link is for free PDF, but textbook can also be purchased on Amazon

Recommended Prerequisites: From Book:

"This book is appropriate for advanced undergraduates or masterâ€™s students in statistics or related quantitative fields or for individuals in other disciplines who wish to use statistical learning tools to analyze their data."

Go to Content: An Introduction to Statistical Learning: with Applications in R

## By jheeldoshi April 28, 2017 - 1:40 pm

I started of reading this book for an academic course. I did gleam a lot out of it the first time around. But the second time around, I read it again but as an intermediate expert. Having analyzed data and knowing what to look for really helped me get more out of this book. I read this book along with presentations/videos from the authors and helped me get very in-depth as well as clear understanding of concepts such as validation.

Advanced Beginner- familiar with data science / new to this topic / some prerequisitesEarly Beginner - was totally new to this topic / had few prerequisites

(4) A Lot

Detailed Content Survey:Required for school or in order to keep or be eligible for a job

This was my first experience with this publisher/series

Yes

Most (i.e. read the entire book but didn't complete the exercises)

I had other priorities or time constraints / stopping was unrelated to the content

Data Analysis

none

watch accompanying videos and slides

Data analysis + static

clearly explained

read once and go over agin

very intensive

Intermediate