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Introduction to Statistical and Machine Learning Methods for Data Science
A practitioner-oriented overview of the statistical and machine learning methods used across the data science lifecycle, emphasizing business applicability over math and code.
A profile of this book is on the way.
What it’s about
This book demystifies data science by walking readers through the full analytical lifecycle—from understanding the business question and preparing data, through supervised and unsupervised modeling, to model assessment, deployment, and operationalization—without burdening them with software or heavy mathematics. Drawing on the authors' decades of real-world experience (especially in telecommunications), it pairs accessible explanations of techniques like regression, decision trees, forests, gradient boosting, neural networks, support vector machines, factorization machines, clustering, association rules, network analysis, and text analytics with concrete business use cases such as churn prediction, fraud detection, bad-debt avoidance, and recommendation. Ideal for citizen data scientists, analysts, and curious professionals, it teaches readers not just what each method does but when to apply it and how to translate model results into deployable business actions that generate real value.
The through-line
- Who it’s for
- A data analyst, business analyst, or aspiring data scientist who wants to apply analytics to real business problems and deliver measurable value.
- The problem
- They need to choose and apply the right statistical and machine learning methods across the analytical lifecycle to solve concrete business problems. They feel overwhelmed by the breadth of techniques, jargon, and tooling and unsure when to use which method.
- The plan
- Understand the business question and required action.
- Collect, explore, and prepare the data.
- Select and train appropriate supervised or unsupervised models.
- Assess models using business-aligned fit statistics and choose the best generalizer.
- Deploy, monitor, and operationalize the chosen model.
- The payoff
- You confidently match techniques to business problems and deployment constraints. · Your models reach production and generate real, measurable business value. · You communicate results clearly and drive data-driven decisions.
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.