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An Introduction to Statistical Learning: with Applications in Python (Springer Texts in Statistics)
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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, marketing, and 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, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data.
Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R(ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.
- ISBN-103031387465
- ISBN-13978-3031387463
- Edition2023rd
- PublisherSpringer
- Publication dateJuly 1, 2023
- LanguageEnglish
- Dimensions7.17 x 1.65 x 10.08 inches
- Print length622 pages
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From the Publisher
Why This Matters
The book confronts an uncomfortable reality: despite decades of progress, many of our problems are intensifying. Climate change threatens human life on Earth. The causes of the 2007-2009 financial crisis remain unresolved, while inequality of income, wealth and power has deepened. AI and robotics raise urgent questions about control and ethics.
Yet politicians and policymakers often promote only ‘STEM’ subjects, science, technology, engineering and mathematics , overlooking the social sciences that are essential for translating technological capability into human wellbeing.
The book makes a compelling case for why this narrow focus must change. From understanding why people reject vaccines despite scientific evidence, to designing cities that support mental health, to regulating AI before it’s too late. These challenges demand insight from psychology, sociology, political science, economics and beyond.
Meet The Editors
Jonathan Michie OBE FAcSS is Professor of Innovation and Knowledge Exchenge at the University of Oxford, UK, and President of Kellogg College. A Fellow of the Academy of Social Sciences, his work spans global economics, lifelong learning, and co-operative business models. His books include A Reader's Guide to the Social Sciences and The Oxford Handbook of Mutual, Co-operative, and Co-owned Business.
Sir Cary L. Cooper CBE FAcSS is Professor of Organizational Psychology at the University of Manchester, UK, and an Honorary Fellow of the Academy of Social Sciences. A leading expert on workplace wellbeing, he has authored over 250 books and is a frequent media commentator. His recent titles include Wellbeing at Work and Remote Workplace Culture.
Inside The Book
Introduction – Jonathan Michie & Sir Cary L. Cooper
Sustainable Development – Jayati Ghosh links poverty, inequality, and climate justice
Plastic Politics – Joshua Lincoln calls for a rights-based treaty
Arab Spring Revisited – Oz Hassan explores global stability and governance
Migration – Robin Cohen examines global realities and political theatre
Decolonial Theory – Iyiola Solanke critiques EU diversity and calls for cognitive justice
Financial Stability – Jonathan Michie proposes a fairer, greener economic order
Productivity Reimagined – Bart van Ark, Mary O’Mahony & Dirk Pilat argue for inclusive metrics
More From Inside The Book
Well-Being by Design – Quick, Cooper, Hesketh & Gatchel show how social science improves health
Food Security & Justice – Camilla Toulmin explores the politics of feeding the future
Law & Inequality – Cowan & Wheeler examine power, algorithms, and fairness
Crime & Compliance – Mike Hough explores trust and legal behaviour
Education as Social Science – Stylianides & Stylianides challenge common sense in policy
Collaborative Solutions – Hasted & Besharov introduce systems thinking and hybridity
Forging a ‘We’ Society – Will Hutton reimagines capitalism for the common good
Praise For The book:
“Technology is only a tool; it is how we as humans use it that will shape societies. That underscores the importance of the social sciences in shaping our future, as individuals, communities and societies.” — Andy Haldane, Chief Executive of the RSA, UK
Editorial Reviews
Review
“The book is an excellent treatise on statistical learning for readers with a basic knowledge of statistics.” (Nirode C. Mohanty, zbMATH 1579.62002, 2026)
“The book adopts a hands-on, practical approach to teaching statistical learning, featuring numerous examples and case studies, accompanied by Python code for implementation. It stands as a contemporary classic, offering clear and intuitive guidance on how to implement cutting-edge statistical and machine learning methods. If you wish to intelligently use data analytics tools and techniques for analyzing big and/or complex data, this book should be front and center on your bookshelf.” (David Han, Mathematical Reviews, May 10, 2024)
From the Back Cover
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, marketing, and 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, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data.
Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.
About the Author
Gareth James is the John H. Harland Dean of Goizueta Business School at Emory University. He has published an extensive body of methodological work in the domain of statistical learning with particular emphasis on high-dimensional and functional data. The conceptual framework for this book grew out of his MBA elective courses in this area.
Daniela Witten is a professor of statistics and biostatistics, and the Dorothy Gilford Endowed Chair, at University of Washington. Her research focuses largely on statistical machine learning techniques for the analysis of complex, messy, and large-scale data, with an emphasis on unsupervised learning.
Trevor Hastie and Robert Tibshirani are professors of statistics at Stanford University and are co-authors of the successful textbook Elements of Statistical Learning. Hastie and Tibshirani developed generalized additive models and wrote a popular book with that title. Hastie co-developed much of the statistical modeling software and environment in R, and invented principal curves and surfaces. Tibshirani invented the lasso and is co-author of the very successful book, An Introduction to the Bootstrap. They are both elected members of the US National Academy of Sciences.
Jonathan Taylor is a professor of statistics at Stanford University. His research focuses on selective inference and signal detection in structured noise.
Product details
- Publisher : Springer
- Publication date : July 1, 2023
- Edition : 2023rd
- Language : English
- Print length : 622 pages
- ISBN-10 : 3031387465
- ISBN-13 : 978-3031387463
- Item Weight : 3.6 pounds
- Dimensions : 7.17 x 1.65 x 10.08 inches
- Part of series : Springer Texts in Statistics
- Best Sellers Rank: #33,724 in Books (See Top 100 in Books)
- #2 in Mathematical & Statistical Software
- #13 in Statistics (Books)
- #23 in Probability & Statistics (Books)
- Customer Reviews:
About the authors

Daniela Witten is a professor of Statistics and Biostatistics at University of Washington, and the Dorothy Gilford Endowed Chair. She is the recipient of a NIH Director's Early Independence Award, a NSF CAREER Award, a Sloan Research Fellowship, and a Simons Investigator Award. For more, see www.danielawitten.com

Discover more of the author’s books, see similar authors, read book recommendations and more.

Robert Tibshirani (born July 10, 1956) is a Professor in the Departments of Statistics and Health Research and Policy at Stanford University. He was a Professor at the University of Toronto from 1985 to 1998. In his work, he develops statistical tools for the analysis of complex datasets, most recently in genomics and proteomics.
His most well-known contributions are the LASSO method, which proposed the use of L1 penalization in regression and related problems, and Significance Analysis of Microarrays. He has also co-authored three well-known books: "Generalized Additive Models", "An Introduction to the Bootstrap", and "The Elements of Statistical Learning", the last of which is available for free from the author's website.
Bio from Wikipedia, the free encyclopedia. Photo by Tibshirani (i took this photo) [GFDL (http://www.gnu.org/copyleft/fdl.html) or CC BY-SA 3.0 (http://creativecommons.org/licenses/by-sa/3.0)], via Wikimedia Commons.

Trevor Hastie is the John A Overdeck Professor of Statistics at
Stanford University. Hastie is known for his research in applied
statistics, particularly in the fields of statistical modeling, bioinformatics
and machine learning. He has published six books and over 200
research articles in these areas. Prior to joining Stanford
University in 1994, Hastie worked at AT&T Bell Laboratories for nine
years, where he contributed to the development of the statistical modeling environment
popular in the R computing system. He received a B.Sc. (hons) in statistics
from Rhodes University in 1976, a M.Sc. from the University of Cape
Town in 1979, and a Ph.D from Stanford in 1984. In 2018 he was elected
to the U.S. National Academy of Sciences. He is a dual citizen of the
United States and South Africa.





















