Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining

Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining

by Glenn J. Myatt
Released November 2006
Publisher(s): Wiley-Interscience
ISBN: 9780470074718

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Book description

A practical, step-by-step approach to making sense out of data

Making Sense of Data educates readers on the steps and issues that need to be considered in order to successfully complete a data analysis or data mining project. The author provides clear explanations that guide the reader to make timely and accurate decisions from data in almost every field of study. A step-by-step approach aids professionals in carefully analyzing data and implementing results, leading to the development of smarter business decisions. With a comprehensive collection of methods from both data analysis and data mining disciplines, this book successfully describes the issues that need to be considered, the steps that need to be taken, and appropriately treats technical topics to accomplish effective decision making from data.

Readers are given a solid foundation in the procedures associated with complex data analysis or data mining projects and are provided with concrete discussions of the most universal tasks and technical solutions related to the analysis of data, including:

  • Problem definitions

  • Data preparation

  • Data visualization

  • Data mining

  • Statistics

  • Grouping methods

  • Predictive modeling

  • Deployment issues and applications

Throughout the book, the author examines why these multiple approaches are needed and how these methods will solve different problems. Processes, along with methods, are carefully and meticulously outlined for use in any data analysis or data mining project.

From summarizing and interpreting data, to identifying non-trivial facts, patterns, and relationships in the data, to making predictions from the data, Making Sense of Data addresses the many issues that need to be considered as well as the steps that need to be taken to master data analysis and mining.

Table of contents

  1. Cover Page
  2. Title Page
  3. Copyright
  4. Contents
  5. Preface
  6. Chapter 1: Introduction
    1. 1.1 OVERVIEW
    2. 1.2 PROBLEM DEFINITION
    3. 1.3 DATA PREPARATION
    4. 1.4 IMPLEMENTATION OF THE ANALYSIS
    5. 1.5 DEPLOYMENT OF THE RESULTS
    6. 1.6 BOOK OUTLINE
    7. 1.7 SUMMARY
    8. 1.8 FURTHER READING
  7. Chapter 2: Definition
    1. 2.1 OVERVIEW
    2. 2.2 OBJECTIVES
    3. 2.3 DELIVERABLES
    4. 2.4 ROLES AND RESPONSIBILITIES
    5. 2.5 PROJECT PLAN
    6. 2.6 CASE STUDY
    7. 2.7 SUMMARY
    8. 2.8 FURTHER READING
  8. Chapter 3: Preparation
    1. 3.1 OVERVIEW
    2. 3.2 DATA SOURCES
    3. 3.3 DATA UNDERSTANDING
    4. 3.4 DATA PREPARATION
    5. 3.5 SUMMARY
    6. 3.6 EXERCISES
    7. 3.7 FURTHER READING
  9. Chapter 4: Tables and Graphs
    1. 4.1 INTRODUCTION
    2. 4.2 TABLES
    3. 4.3 GRAPHS
    4. 4.4 SUMMARY
    5. 4.5 EXERCISES
    6. 4.6 FURTHER READING
  10. Chapter 5: Statistics
    1. 5.1 OVERVIEW
    2. 5.2 DESCRIPTIVE STATISTICS
    3. 5.3 INFERENTIAL STATISTICS
    4. 5.4 COMPARATIVE STATISTICS
    5. 5.5 SUMMARY
    6. 5.6 EXERCISES
    7. 5.7 FURTHER READING
  11. Chapter 6: Grouping
    1. 6.1 INTRODUCTION
    2. 6.2 CLUSTERING
    3. 6.3 ASSOCIATIVE RULES
    4. 6.4 DECISION TREES
    5. 6.5 SUMMARY
    6. 6.6 EXERCISES
    7. 6.7 FURTHER READING
  12. Chapter 7: Prediction
    1. 7.1 INTRODUCTION
    2. 7.2 SIMPLE REGRESSION MODELS
    3. 7.3 K-NEAREST NEIGHBORS
    4. 7.4 CLASSIFICATION AND REGRESSION TREES
    5. 7.5 NEURAL NETWORKS
    6. 7.6 OTHER METHODS
    7. 7.7 SUMMARY
    8. 7.8 EXERCISES
    9. 7.9 FURTHER READING
  13. Chapter 8: Deployment
    1. 8.1 OVERVIEW
    2. 8.2 DELIVERABLES
    3. 8.3 ACTIVITIES
    4. 8.4 DEPLOYMENT SCENARIOS
    5. 8.5 SUMMARY
    6. 8.6 FURTHER READING
  14. Chapter 9: Conclusions
    1. 9.1 SUMMARY OF PROCESS
    2. 9.2 EXAMPLE
    3. 9.3 ADVANCED DATA MINING
    4. 9.4 FURTHER READING
  15. Appendix A: Statistical Tables
    1. A.1 NORMAL DISTRIBUTION
    2. A.2 STUDENT'S T -DISTRIBUTION
    3. A.3 CHI-SQUARE DISTRIBUTION
    4. A.4 F-DISTRIBUTION
  16. Appendix B: Answers to Exercises
  17. Glossary
  18. Bibliography
  19. Index

Product information

  • Title: Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining
  • Author(s): Glenn J. Myatt
  • Release date: November 2006
  • Publisher(s): Wiley-Interscience
  • ISBN: 9780470074718