Coming soon · Book Profile
Practical Statistics for Data Scientists
A practitioner's guide to the 50+ core statistical concepts data scientists actually need, explaining which classical methods matter for modern data work and why.
A profile of this book is on the way.
What it’s about
Written for data scientists with some R/Python familiarity and patchy statistics background, this book distills a century and a half of statistical theory into the concepts that are genuinely useful for data science practice. Rather than burdening readers with the inferential machinery that dominates traditional courses, the authors—two statisticians turned data scientists and a computational scientist—triage statistical ideas, showing which are load-bearing for prediction and exploration and which can be safely de-emphasized. From exploratory data analysis and sampling distributions through significance testing, regression, classification, and statistical machine learning (KNN, trees, bagging, boosting) to unsupervised methods (PCA, clustering), every concept is paired with practical R and Python code and a clear-eyed verdict on its real-world relevance. The result is a navigable, reference-friendly bridge between the disciplines of statistics and data science.
The through-line
- Who it’s for
- A data scientist with working knowledge of R and/or Python and spotty prior exposure to statistics who wants to apply the right statistical concepts effectively to real data problems.
- The problem
- Traditional statistics instruction is bloated with inferential machinery, and it is unclear which concepts actually matter for building and evaluating predictive models on real data. The reader feels uncertain, intimidated, or fooled by random patterns, unsure whether their analyses are sound or whether they are overcomplicating things.
- The plan
- Start by exploring and visualizing your data to build intuition (EDA).
- Understand sampling and sampling distributions, and use the bootstrap to gauge uncertainty.
- Apply experimental design and significance testing judiciously, focusing on resampling intuition.
- Build regression models for prediction and explanation, watching for confounders and diagnostics.
- Build and rigorously evaluate classification models, especially with imbalanced data.
- The payoff
- You confidently choose the right statistical method for each problem and know why. · You quantify uncertainty correctly and avoid being misled by random variation. · You build predictive models that generalize well and evaluate them with appropriate metrics.
See our guide
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- Business Intelligence Guidebook: From Data Integration to Analytics →
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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.