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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: with Applications in Python (Springer Texts in Statistics)

4.6 out of 5 stars (159)

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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.

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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.

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)
  • Customer Reviews:
    4.6 out of 5 stars (159)

About the authors

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