Coming soon · Book Profile
Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking
A conceptual guide that distills the fundamental principles underlying data science so that business people and aspiring data scientists can think data-analytically about extracting useful knowledge from data to improve business decisions.
A profile of this book is on the way.
What it’s about
Data Science for Business is the definitive primer for understanding data science not as a grab-bag of algorithms but as a coherent set of fundamental principles that structure data-analytic thinking. Provost and Fawcett—both seasoned practitioners and researchers—argue that beneath the dizzying array of data mining techniques lies a relatively small set of concepts (treating data as a strategic asset, framing problems with expected value, finding informative attributes, fitting models while controlling overfitting, measuring similarity) that unify the field. Organized around the CRISP data mining process and richly illustrated with real-world business cases—customer churn, targeted marketing, fraud detection, charity solicitation, whiskey recommendation, text mining of news—the book teaches readers to decompose business problems into solvable data science tasks, to evaluate solutions in business terms, and to communicate across the technical/business divide. It is the rare book that equips managers to evaluate data science proposals and equips data scientists to align their work with business value, making both better at extracting competitive advantage from data.
The through-line
- Who it’s for
- A business professional, manager, investor, or aspiring data scientist who wants to extract competitive advantage and better decisions from their organization's data.
- The problem
- They have vast amounts of data but lack a principled way to turn it into useful knowledge and better business decisions. They feel intimidated by jargon and algorithms, unsure whether a proposed data science effort is sound or whether they are being misled.
- The plan
- Learn the small set of fundamental concepts that underlie data science.
- Adopt data-analytic thinking and the CRISP process to structure problems.
- Decompose business problems into known data mining tasks using the expected value framework.
- Evaluate models in business terms, guarding against overfitting and misleading metrics.
- Build, nurture, and manage data science capability as a strategic asset.
- The payoff
- The reader confidently frames business problems data-analytically and decomposes them into solvable tasks. · The reader can evaluate data science proposals, spot flaws, and ask probing questions. · The reader's organization invests wisely in data and data scientists, gaining and sustaining competitive advantage.
See our guide
Related profiles we’ve built
- A Practical Guide To Conjoint Analysis →
- Analytics at Work: Smarter Decisions, Better Results →
- Big Data: A Revolution That Will Transform How We Live, Work, and Think →
- Big Data_ A Very Short Introduction (Very Short Introductions) →
- Business Adventures →
- Business Intelligence Guidebook: From Data Integration to Analytics →
- Data Mining for Business Analytics: Concepts, Techniques, and Applications →
- Data Warehouse and Data Mining →
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.