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Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinkingby Foster Provost
Synopses & Reviews
Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today.
Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. Youll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your companys data science projects. Youll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.
This broad, deep, but not-too-technical guide introduces you to the fundamental principles of data science and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. By learning data science principles, you will understand the many data-mining techniques in use today. More importantly, these principles underpin the processes and strategies necessary to solve business problems through data mining techniques.
Data Science for Business, by highly cited authors Foster Provost and Tom Fawcett, is intended for (i) those who need to understand data science/data mining broadly and (ii) those who want to develop their skill at data-analytic thinking. It is not a book about algorithms. Instead it presents a set of fundamental principles for getting business value by extracting useful knowledge from data. These fundamental principles are the foundation for many data mining techniques, but they also are the basis for frameworks for approaching business problems data-analytically, evaluating data science solutions, and evaluating general plans for data analytics.
After reading the book, the reader should be able to:
About the Author
Foster Provost is a Professor and NEC Faculty Fellow at the NYU Stern School of Business, where he has taught data science to MBAs for 15 years. Previously, he worked as a data scientist for what's now Verizon for five years, winning a President's Award for his work there. Professor Provost's research and teaching focus on data science, machine learning, business analytics, (social) network data, and crowd-sourcing for data analytics. He was Editor-in-Chief of the journal Machine Learning from 2004 to 2010 and was Program Chair of the premier data science conference in 2001. Professor Provost has worked with companies large and small on improving their data science capabilities. He has collaborated with AT&T, IBM, and others, and he has founded several data-science based companies focusing on modeling consumer behavior data especially for marketing and advertising applications. His prior work applied and extended data science methods to business applications including fraud detection, counterterrorism, network diagnosis, and more. Professor Provosts work has won (among others) IBM Faculty Awards, the aforementioned President's Award, Best Paper awards at KDD, including the 2012 Best Industry Paper, and the INFORMS Design Science Award.
Tom Fawcett is an active member of the machine learning and data mining communities. He has a Ph.D. in machine learning from UMass-Amherst and has worked in industrial research (GTE Laboratories, NYNEX/Verizon Labs, HP Labs, etc.). In his career he has published numerous conference and journal papers in machine learning. He has just completed a five year term as action editor of the Machine Learning journal, before which he was an editorial board member. In 2003 he co-chaired the program of the premier machine learning conference (ICML) and has organized many workshops and journal special issues. He received a Best Paper Award from KDD, a SCOPUS Award (most cited paper) from Pattern Recognition Letters, and a President's Award from Verizon.
Table of Contents
PraisePrefaceChapter 1: Introduction: Data-Analytic ThinkingChapter 2: Business Problems and Data Science SolutionsChapter 3: Introduction to Predictive Modeling: From Correlation to Supervised SegmentationChapter 4: Fitting a Model to DataChapter 5: Overfitting and Its AvoidanceChapter 6: Similarity, Neighbors, and ClustersChapter 7: Decision Analytic Thinking I: What Is a Good Model?Chapter 8: Visualizing Model PerformanceChapter 9: Evidence and ProbabilitiesChapter 10: Representing and Mining TextChapter 11: Decision Analytic Thinking II: Toward Analytical EngineeringChapter 12: Other Data Science Tasks and TechniquesChapter 13: Data Science and Business StrategyChapter 14: ConclusionProposal Review GuideAnother Sample ProposalGlossaryBibliographyIndexColophon
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