Handbook of statistical analysis and data mining applications / Robert Nisbet, Gary Miner and Ken Yale ; guest authors of selected chapters, John Elder IV, Andy Peterson.

Includes index.

Part 1: History of phases of data analysis, basic theory, and the data mining process -- 1.The background for data mining practice -- 2.Theoretical considerations for data mining -- 3.The data mining and predictive analytic process -- 4.Data understanding and preparation -- 5.Feature selection -- 6.Accessory tools for doing data mining -- Part 2: The algorithms and methods in data mining and predictive analytics and some domain areas -- 7.Basic algorithms for data mining: a brief overview -- 8.Advanced algorithms for data mining -- 9.Classification -- 10.Numerical prediction -- 11.Model evaluation and enhancement -- 12.Predictive analytics for population health and care -- 13.Big data in education: new efficiencies for recruitment, learning and retention of students and donors -- 14.Customer response modeling -- 15.Fraud detection -- Part 3: Tutorials and case studies -- Part 4: Model ensembles, model complexity; using the right model for the right use, significance, ethics, and the future, and advanced processes -- 16.The apparent paradox of complexity in ensemble modeling -- 17.The rigth model for the right purpose: when less is good enough -- 18.A data preparation cookbook -- 19.Deep learning -- 20.Significance versus luck in the age of mining: the issues of p-value significance and ways to test significance of our predictive analytic models -- 21.Ethics and data analytics.

The Handbook of Statistical Analysis and Data Mining Applications is a comprehensive professional reference book that guides business analysts, scientists, engineers and researchers (both academic and industrial) through all stages of data analysis, model building and implementation. The Handbook helps one discern the technical and business problem, understand the strengths and weaknesses of modern data mining algorithms, and employ the right statistical methods for practical application. Use this book to address massive and complex datasets with novel statistical approaches and be able to objectively evaluate analyses and solutions. It has clear, intuitive explanations of the principles and tools for solving problems using modern analytic techniques, and discusses their application to real problems, in ways accessible and beneficial to practitioners across industries - from science and engineering, to medicine, academia and commerce. This handbook brings together, in a single resource, all the information a beginner will need to understand the tools and issues in data mining to build successful data mining solutions.