Data Science For Business - Detailed Summary

  • "Data Science for Business" is a highly recommended resource for individuals keen on leveraging big data opportunities, according to Craig Vaughan, Global Vice President, SAP.

  • The book introduces fundamental data science principles and data-analytic thinking for extracting knowledge and business value from collected data.

  • It emphasizes understanding data mining techniques and strategies for solving business problems.

  • Tom Phillips, CEO of Dstillery and former Head of Google Search and Analytics, considers the book an essential guide for businesses built on data opportunities, emphasizing data-driven decision-making.

  • Chris Volinsky, Director of Statistics Research at AT&T Labs, praises the book for making data science concepts accessible and applicable to practical business problems with real-world examples, focusing on problem-solving rather than specific algorithms.

  • Alan Murray, Partner at Coriolis Ventures, highlights the book's relevance for understanding competitors' strategies and the increasing importance of data in productivity growth and customer insight.

  • Ron Bekkerman, Chief Data Officer at Carmel Ventures, emphasizes the book's ability to connect data with business thinking, calling it the "Science behind thinking data."

  • Ronny Kohavi, Partner Architect at Microsoft Online Services Division, recommends the book for business managers interacting with data scientists, seeking to understand principles and algorithms without technical details.

  • Geoff Webb, Editor-in-Chief of Data Mining and Knowledge Discovery Journal, acclaims Provost and Fawcett for distilling their mastery of real-world data analysis into an unrivalled introduction.

  • Claudia Perlich, Chief Scientist of Dstillery and Advertising Research Foundation Innovation Award Grand Winner (2013), expresses a wish for all her collaborators to have read the book.

  • Justin Gapper, Business Unit Analytics Manager at Teledyne Scientific and Imaging, regards it as a must-read for anyone interested in the Big Data revolution.

  • Josh Attenberg, Data Science Lead at Etsy, recommends the book as a primer for engineers, analysts, and managers to understand options and tradeoffs in developing data-driven systems.

  • Nidhi Kathuria, Vice President of FX at Royal Bank of Scotland, values the book for its insights into liquidity analysis and excellent examples.

  • Joe McCarthy, Director of Analytics and Data Science at Atigeo, praises it as an accessible primer for business professionals to appreciate data science concepts and for data scientists to understand business context.

  • Ira Laefsky, MS Engineering (Computer Science)/MBA Information Technology and Human Computer Interaction Researcher, recommends it for business analysts and managers needing a practical understanding of Data Science and Big Data.

  • Ted O’Brien, Co-Founder/Director of Talent Acquisition at Starbridge Partners and Publisher of the Data Science Report, recommends it for those wishing to become involved in developing data-driven systems, citing its motivating examples and clear exposition.

  • Copyright by Foster Provost and Tom Fawcett in 2013. Published by O’Reilly Media, Inc.

  • O’Reilly books are available for purchase for educational, business, or sales promotional use.

  • The book is dedicated to the authors’ fathers.

  • The book's preface outlines its intended audience: business people working with data scientists, developers implementing data science solutions, and aspiring data scientists.

  • The authors focus on fundamental concepts and principles underlying data mining techniques rather than providing a cookbook of algorithms.

  • The book emphasizes data science’s conceptual approach to data science

-it includes three general types:

  • Concepts that fit in organizational landscape

  • General ways of data-analytically thinking

  • General concepts for extracting data

    • The fundamental concepts of data science cover topics such as data science's place in organizations, data-analytic thinking, and knowledge extraction techniques.

    • The goal is to align understanding between business, technical, and data science teams.

    • Recommends this book to prepare and interview data science job candidates.

    • The book arose from multidisciplinary Data Science classes at the Stern School at NYU, starting in 2005.

    • Readers can check the website (http://oreil.ly/data-science and http://www.data-science-for-biz.com) for updates and additional information.

    • The goal of data science as improving decision making.

    • The book covers a wide array of business analytics methods and techniques.

    • Data Mining explained as the extraction of knowledge from data via technologies that incorporate these principles.

    • The book is organized into 14 chapters:

  • Introduction: Data-Analytic Thinking

  • Business Problems and Data Science Solutions

  • Introduction to Predictive Modeling: From Correlation to Supervised Segmentation

  • Fitting a Model to Data

  • Overfitting and Its Avoidance

  • Similarity, Neighbors, and Clusters

  • Decision Analytic Thinking I: What Is a Good Model?

  • Visualizing Model Performance

  • Evidence and Probabilities

  • Representing and Mining Text

  • Decision Analytic Thinking II: Toward Analytical Engineering

  • Other Data Science Tasks and Techniques

  • Data Science and Business Strategy

  • Conclusion