Handbook of Statistical Analysis and Data Mining Applications (2nd Ed.)

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Language: Anglais
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822 p. · 19.1x23.5 cm · Hardback

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.



  • Includes input by practitioners for practitioners
  • Includes tutorials in numerous fields of study that provide step-by-step instruction on how to use supplied tools to build models
  • Contains practical advice from successful real-world implementations
  • Brings together, in a single resource, all the information a beginner needs to understand the tools and issues in data mining to build successful data mining solutions
  • Features clear, intuitive explanations of novel analytical tools and techniques, and their practical applications

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

Business analysts, scientists, engineers, researchers, and students in statistics and data mining