Coming soon · Book Profile
An Introduction to Statistical Learning: with Applications in R
A practical, accessible introduction to statistical learning that teaches the major supervised and unsupervised methods for understanding and predicting from data, with hands-on Python labs.
A profile of this book is on the way.
What it’s about
An Introduction to Statistical Learning, With Applications in Python (ISLP) demystifies modern data analysis by presenting the most important statistical and machine learning methods in an accessible, application-focused way. Rather than dwelling on heavy mathematics, it builds intuition for the core trade-offs that govern model performance, especially the bias-variance trade-off and the gap between training and test error. Covering linear and logistic regression, resampling, regularization, splines and generalized additive models, tree ensembles, support vector machines, deep learning, survival analysis, unsupervised learning, and multiple testing, the book pairs each topic with a Python lab so readers can immediately implement what they learn. It is ideal for advanced undergraduates, master's students, and working data scientists who want to become informed, capable users of statistical learning tools in a data-driven world.
The through-line
- Who it’s for
- An advanced student or working data analyst who wants to understand and apply modern statistical learning methods to make sense of complex data.
- The problem
- They face large, complex datasets and need to predict outcomes or discover structure without knowing which method to use or how to evaluate it. They feel intimidated by the highly technical, mathematical presentations of machine learning and worry they will misuse methods or overfit.
- The plan
- Build intuition for estimating f and the bias-variance trade-off.
- Learn core supervised methods for regression and classification.
- Use resampling to honestly estimate test error and tune complexity.
- Apply regularization and dimension reduction for high-dimensional data.
- Work through hands-on Python labs at the end of each chapter.
- The payoff
- Confidently selects appropriate methods and evaluates them with cross-validation or test sets. · Avoids overfitting and interprets models honestly, including in high dimensions. · Implements analyses in Python and contributes effectively in a data-driven field.
See our guide
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Additional reading
- Competing on Analytics: The New Science of Winning · Thomas H. Davenport and Jeanne G. Harris
The authors' previous book, which provides the strategic context by describing the earliest and most aggressive adopters of analytics. This book builds on it by providing a 'how-to' guide for all organizations.
- Sources of Power: How People Make Decisions · Gary Klein
Discusses decision-making in high-pressure situations where there is no time for systematic data gathering, providing a contrast to the analytical approach and showing when intuition is necessary.
- The Black Swan: The Impact of the Highly Improbable · Nassim Nicholas Taleb
Argues that statistical analysis is limited because it cannot predict rare, high-impact 'black swan' events, serving as a cautionary note on the limits of analytics.
- Moneyball: The Art of Winning an Unfair Game · Michael Lewis
A popular case study of how the Oakland A's baseball team used an analytical approach to player selection to compete with richer teams, illustrating the power of competing on analytics.
- Why Great Leaders Don't Take Yes for an Answer · Michael Roberto
Describes how to foster a culture of constructive conflict and debate in decision-making processes, which is essential for an analytical culture where assumptions are tested and merit triumphs over politics.
- The Visual Display of Quantitative Information · Edward Tufte
A foundational work on how to create clear visual representations of data, a key skill for communicating analytical findings effectively.
- Super Crunchers: Why Thinking-By-Numbers Is the New Way to Be Smart · Ian Ayres
The book discusses how statistical analyses are replacing human intuition and expert judgment in decision-making, a core theme related to the discussion of 'Moneyball' and the demise of the expert.
- Seeing Like a State: How Certain Schemes to Improve the Human Condition Have Failed · James Scott
Documents how governments' fetish for quantification and data can lead to misguided and harmful policies, providing a deep historical context for the book's warnings about the 'dictatorship of data'.
- The War Managers · Douglas Kinnard
A survey of U.S. generals' views on the Vietnam War, revealing that the 'body count' metric was seen as a worthless and inflated measure of progress, illustrating the dangers of relying on flawed data.
- Thinking, Fast and Slow · Daniel Kahneman
Explains the cognitive biases that lead humans to see illusory causal links, which the author's argue big data correlations can challenge and disprove.