Nisbet R., Miner C., Yale K. Handbook of Statistical Analysis and Data Mining Applications

Second Edition. — Academic Press, 2018. — 795 p. — ISBN: 978-0-12-416632-5.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.Key FeaturesIncludes 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 applicationsBusiness analysts, scientists, engineers, researchers, and students in statistics and data miningHistory of Phases of Data Analysis, Basic Theory, and the Data Mining
The Background for Data Mining Practice
Theoretical Considerations for Data MiningFurther Reading
The Data Mining and Predictive Analytic ProcessData Understanding and PreparationFurther Reading
Feature SelectionAccessory Tools for Doing Data Mining
Further Reading
The Algorithms and Methods in Data Mining and Predictive
Basic Algorithms for Data Mining: A Brief OverviewFurther Reading
Advanced Algorithms for Data MiningFurther Reading
Classification
Numerical PredictionModel Evaluation and EnhancementPredictive Analytics for Population Health and Care
Big Data in Education: New Efficiencies for Recruitment, Learning, and Retention of Students and Donors
Customer Response Modeling
Fraud Detection
Tutorials and Case Studies
Tutorial A
Example of Data Mining Recipes Using Windows 10 and Statistica 13
Tutorial B
Using Ihe 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 h 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: Mergng Data Sources
Tutorial I
Data Prep 1-2: Data Description
Tutorial 3
Data Prep 2-1: Data Cleaning and Recodlng
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
Tutorial Q
Using Customer Churn Data to Develop and Select a Best Predictive Model
Tutorial R
Example With CSRT to Predict and Display Possible Structural Relationships
Tutorial S
Clinical Psychology: Making Decisions About Best Therapy for a for a
Client
Model Ensembles, Model Complexity; Using the Right Model for the Right Use, Significance, Ethics, and Future, and Advanced Processes
The Apparent Paradox of Complexity in Ensemble Modeling
The Tight Model" for the Tight Purpose": When Purpose": When Less Is Good Enough
A Data Preparation Cookbook
Deep Learning
Significance versus Luck in the Age of Mining: The Issues of P-Value
Ethics and Data Analytics
IBM WatsonTrue PDF