Handbook of Statistical Analysis and Data Mining Applications
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Handbook of Statistical Analysis and Data Mining Applications

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Description

Handbook of Statistical Analysis and Data Mining Applications, Second Edition, 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 users discern technical and business problems, understand the strengths and weaknesses of modern data mining algorithms and employ the right statistical methods for practical application.

This book is an ideal reference for users who want 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 several areas-from science and engineering, to medicine, academia and commerce.
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Product details

  • Hardback | 822 pages
  • 191 x 235 x 45.72mm | 1,790g
  • Academic Press Inc
  • San Diego, United States
  • English
  • 2nd edition
  • 0124166326
  • 9780124166325
  • 765,160

Table of contents

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

Tutorial A Example of Data Mining Recipes Using Windows 10 and Statistica 13

Tutorial B Using the Statistica Data Mining Workspace Method for Analysis of Hurricane Data (Hurrdata.sta)

Tutorial C Case Study-Using SPSS Modeler and STATISTICA to Predict Student Success at High-Stakes Nursing Examinations (NCLEX)

Tutorial D Constructing a Histogram in KNIME Using MidWest Company Personality Data

Tutorial E Feature Selection in KNIME

Tutorial F Medical/Business Tutorial

Tutorial G A KNIME Exercise, Using Alzheimer's Training Data of Tutorial F

Tutorial H Data Prep 1-1: Merging Data Sources

Tutorial I Data Prep 1-2: Data Description

Tutorial J Data Prep 2-1: Data Cleaning and Recoding

Tutorial K Data Prep 2-2: Dummy Coding Category Variables

Tutorial L Data Prep 2-3: Outlier Handling

Tutorial M Data Prep 3-1: Filling Missing Values With Constants

Tutorial N Data Prep 3-2: Filling Missing Values With Formulas

Tutorial O Data Prep 3-3: Filling Missing Values With a Model

Tutorial P City of Chicago Crime Map: A Case Study Predicting Certain Kinds of Crime Using Statistica Data Miner and Text Miner

Tutorial Q Using Customer Churn Data to Develop and Select a Best Predictive Model for Client Defection Using STATISTICA Data Miner 13 64-bit for Windows 10

Tutorial R Example With C&RT to Predict and Display Possible Structural Relationships

Tutorial S Clinical Psychology: Making Decisions About Best Therapy for a Client

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 "Right 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

22. IBM Watson
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Review Text

"Data mining practitioners, here is your bible, the complete "driver's manual" for data mining. From starting the engine to handling the curves, this book covers the gamut of data mining techniques - including predictive analytics and text mining - illustrating how to achieve maximal value across business, scientific, engineering, and medical applications. What are the best practices through each phase of a data mining project? How can you avoid the most treacherous pitfalls? The answers are in here.

"Going beyond its responsibility as a reference book, the heavily-updated second edition also provides all-new, detailed tutorials with step-by-step instructions to drive established data mining software tools across real world applications. This way, newcomers start their engines immediately and experience hands-on success.

"What's more, this edition drills down on hot topics across seven new chapters, including deep learning and how to avert "b---s---" results. If you want to roll-up your sleeves and execute on predictive analytics, this is your definite, go-to resource. To put it lightly, if this book isn't on your shelf, you're not a data miner." -- Eric Siegel, Ph.D., founder of Predictive Analytics World and author of "Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die"

"Great introduction to the real-world process of data mining. The overviews, practical advice, tutorials, and extra CD material make this book an invaluable resource for both new and experienced data miners." --Karl Rexer, PhD (President and Founder of Rexer Analytics, Boston, Massachusetts)
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Review quote

"Great introduction to the real-world process of data mining. The overviews, practical advise, tutorials, and extra CD material make this book an invaluable resource for both new and experienced data miners."

- Karl Rexer, PhD (President & Founder of Rexer Analytics, Boston, Massachusetts)


If you want to roll-up your sleeves and execute on predictive analytics, this is your definite, go-to resource. To put it lightly, if this book isn't on your shelf, you're not a data miner.

- Eric Siegel, Ph.D., President, Prediction Impact, Inc. and Founding Chair, Predictive Analytics World
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About Ken Yale

Dr. Robert Nisbet was trained initially in Ecology and Ecosystems Analysis. He has over 30 years' experience in complex systems analysis and modeling, most recently as a Researcher (University of California, Santa Barbara). In business, he pioneered the design and development of configurable data mining applications for retail sales forecasting, and Churn, Propensity-to-buy, and Customer Acquisition in Telecommunications Insurance, Banking, and Credit industries. Currently, he serves as an Instructor in the University of California, Irvine Predictive Analytics Certificate Program, teaching online courses in Effective Data preparation (UCI), and Introduction to Predictive Analytics (UCSB). Dr. Gary Miner received a B.S. from Hamline University, St. Paul, MN, with biology, chemistry, and education majors; an M.S. in zoology and population genetics from the University of Wyoming; and a Ph.D. in biochemical genetics from the University of Kansas as the recipient of a NASA pre-doctoral fellowship. He pursued additional National Institutes of Health postdoctoral studies at the U of Minnesota and U of Iowa eventually becoming immersed in the study of affective disorders and Alzheimer's disease. In 1985, he and his wife, Dr. Linda Winters-Miner, founded the Familial Alzheimer's Disease Research Foundation, which became a leading force in organizing both local and international scientific meetings, bringing together all the leaders in the field of genetics of Alzheimer's from several countries, resulting in the first major book on the genetics of Alzheimer's disease. In the mid-1990s, Dr. Miner turned his data analysis interests to the business world, joining the team at StatSoft and deciding to specialize in data mining. He started developing what eventually became the Handbook of Statistical Analysis and Data Mining Applications (co-authored with Drs. Robert A. Nisbet and John Elder), which received the 2009 American Publishers Award for Professional and Scholarly Excellence (PROSE). Their follow-up collaboration, Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications, also received a PROSE award in February of 2013. Overall, Dr. Miner's career has focused on medicine and health issues, so serving as the 'project director' for this current book on 'Predictive Analytics of Medicine - Healthcare Issues' fit his knowledge and skills perfectly. Gary also serves as VP & Scientific Director of Healthcare Predictive Analytics Corp; as Merit Reviewer for PCORI (Patient Centered Outcomes Research Institute) that awards grants for predictive analytics research into the comparative effectiveness and heterogeneous treatment effects of medical interventions including drugs among different genetic groups of patients; additionally he teaches on-line classes in 'Introduction to Predictive Analytics', 'Text Analytics', and 'Risk Analytics' for the University of California-Irvine, and other classes in medical predictive analytics for the University of California-San Diego; he spends most of his time in his primary role as Senior Analyst-Healthcare Applications Specialist for Dell | Information Management Group, Dell Software (through Dell's acquisition of StatSoft in April 2014). Dr. Yale is currently Chief Clinical Officer of Delta Dental. He has more than 20 years of executive management experience in government, entrepreneurial, startup and large health care companies. Prior to Delta, Dr. Yale served as vice president, medical director and senior counsel at ActiveHealth Management, an advanced predictive analytics and clinical decision support subsidiary of Aetna. Previously he led the innovation incubator division of UnitedHealth Community and State and also held positions at Matria Healthcare, CorSolutions, EduNeering, Advanced Health Solutions, Health Solutions Network and Jefferson Group. His government experience includes serving as legislative counsel in the U.S. Senate, executive director of the White House Domestic Policy Council, chief of staff of the White House Office of Science and Technology and commissioned officer in the U.S. Public Health Service. Dr. Yale currently has leadership roles or is actively involved with the American Medical Informatics Association, Bloomberg/BNA Health Insurance Advisory Board, Healthcare Information Management and Systems Society, Society for Participatory Medicine, URAC Industry Accreditation Organization, and on other advisory boards for pharmaceutical, genetics and translational bioinformatics companies and organizations. He holds a dental degree from the University of Maryland School of Dentistry, a law degree from Georgetown University Law Center and a bachelor's degree in sociology from Creighton University.
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