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

Publication subTitle ：A Practical Guide to Exploratory Data Analysis and Data Mining

Author： Glenn J. Myatt  

Publisher： John Wiley & Sons Inc‎

Publication year： 2014

E-ISBN: 9781118422014

P-ISBN(Paperback): 9781118407417

P-ISBN(Hardback):  9781118407417

Subject： C931.1 Management Mathematics

Language： ENG

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Description

Praise for the First Edition

 “...a well-written book on data analysis and data mining that provides an excellent foundation...”

—CHOICE

“This is a must-read book for learning practical statistics and data analysis...”

—Computing Reviews.com

 

A proven go-to guide for data analysis, Making Sense of Data I: A Practical Guide to Exploratory Data Analysis and Data Mining, Second Edition focuses on basic data analysis approaches that are necessary to make timely and accurate decisions in a diverse range of projects. Based on the authors’ practical experience in implementing data analysis and data mining, the new edition provides clear explanations that guide readers from almost every field of study.

In order to facilitate the needed steps when handling a data analysis or data mining project, a step-by-step approach aids professionals in carefully analyzing data and implementing results, leading to the development of smarter business decisions. The tools to summarize and interpret data in order to master data analysis are integrated throughout, and the Second Edition also features:

  • Updated exercises for both manual and computer-aided implementation with accompanying worked examples
  • New appendices with coverage on the freely available Traceis™ software, including tutorials using data from a variety of disciplines such as the social sciences, engineering, and finance
  • New topical coverage on multiple linear regression and logistic regression to provide a range of widely used and transparent approaches
  • Additional real-world examples of data preparation to establish a practical background for making decisions from data

Making Sense of Data I: A Practical Guide to Exploratory Data Analysis and Data Mining, Second Edition is an excellent reference for researchers and professionals who need to achieve effective decision making from data. The Second Edition is also an ideal textbook for undergraduate and graduate-level courses in data analysis and data mining and is appropriate for cross-disciplinary courses found within computer science and engineering departments.

Chapter

Making Sense of Data I

Contents

Preface

1 Introduction

1.1 Overview

1.2 Sources of Data

1.3 Process for Making Sense of Data

1.3.1 Overview

1.3.2 Problem Definition and Planning

1.3.3 Data Preparation

1.3.4 Analysis

1.3.5 Deployment

1.4 OVERVIEW OF BOOK

1.4.1 Describing Data

1.4.2 Preparing Data Tables

1.4.3 Understanding Relationships

1.4.4 Understanding Groups

1.4.5 Building Models

1.4.6 Exercises

1.4.7 Tutorials

1.5 Summary

Further Reading

2 Describing Data

2.1 Overview

2.2 Observations and Variables

2.3 Types of Variables

2.4 Central Tendency

2.4.1 Overview

2.4.2 Mode

2.4.3 Median

2.4.4 Mean

2.5 Distribution of the Data

2.5.1 Overview

2.5.2 Bar Charts and Frequency Histograms

2.5.3 Range

2.5.4 Quartiles

2.5.5 Box Plots

2.5.6 Variance

2.5.7 Standard Deviation

2.5.8 Shape

2.6 Confidence Intervals

2.7 Hypothesis Tests

Exercises

Further Reading

3 Preparing Data Tables

3.1 Overview

3.2 Cleaning the Data

3.3 Removing Observations and Variables

3.4 Generating Consistent Scales Across Variables

3.5 New Frequency Distribution

3.6 Converting Text to Numbers

3.7 Converting Continuous Data to Categories

3.8 Combining Variables

3.9 Generating Groups

3.10 Preparing Unstructured Data

Exercises

Further Reading

4 Understanding Relationships

4.1 Overview

4.2 Visualizing Relationships Between Variables

4.2.1 Scatterplots

4.2.2 Summary Tables and Charts

4.2.3 Cross-Classification Tables

4.3 Calculating Metrics About Relationships

4.3.1 Overview

4.3.2 Correlation Coefficients

4.3.3 Kendall Tau

4.3.4 t-Tests Comparing Two Groups

4.3.5 ANOVA

4.3.6 Chi-Square

Exercises

Further Reading

5 Identifying and Understanding Groups

5.1 Overview

5.2 Clustering

5.2.1 Overview

5.2.2 Distances

5.2.3 Agglomerative Hierarchical Clustering

5.2.4 k-Means Clustering

5.3 Association Rules

5.3.1 Overview

5.3.2 Grouping by Combinations of Values

5.3.3 Extracting and Assessing Rules

5.3.4 Example

5.4 Learning Decision Trees from Data

5.4.1 Overview

5.4.2 Splitting

5.4.3 Splitting Criteria

5.4.4 Example

Exercises

Further Reading

6 Building Models from Data

6.1 Overview

6.2 Linear Regression

6.2.1 Overview

6.2.2 Fitting a Simple Linear Regression Model

6.2.3 Fitting a Multiple Linear Regression Model

6.2.4 Assessing the Model Fit

6.2.5 Testing Assumptions

6.2.6 Selecting and Assessing Independent Variables

6.3 Logistic Regression

6.3.1 Overview

6.3.2 Fitting a Simple Logistic Regression Model

6.3.3 Fitting and Interpreting Multiple Logistic Regression Models

6.3.4 Significance of Model and Coefficients

6.3.5 Classification

6.4 k-Nearest Neighbors

6.4.1 Overview

6.4.2 Training

6.4.3 Predicting

6.5 Classification and Regression Trees

6.5.1 Overview

6.5.2 Predicting

6.5.3 Example

6.6 Other Approaches

6.6.1 Neural Networks

6.6.2 Support Vector Machines

6.6.3 Discriminant Analysis

6.6.4 Naïve Bayes

6.6.5 Random Forests

Exercises

Further Reading

A Answers to Exercises

B Hands-on Tutorials

B.1 Tutorial Overview

B.2 Access and Installation

B.3 Software Overview

B.4 Reading in Data

B.5 Preparation Tools

B.5.1 Searching the Data

B.5.2 Variable Characterization

B.5.3 Removing Observations and Variables

B.5.4 Cleaning the Data

B.5.5 Transforming the Data

B.5.6 Segmentation

B.6 Tables and Graph Tools

B.6.1 Contingency Tables

B.6.2 Summary Tables

B.6.3 Graphs

B.6.4 Graph Matrices

B.7 Statistics Tools

B.7.1 Descriptive Statistics

B.7.2 Confidence Intervals

B.7.3 t-test

B.7.4 Chi-Square Test

B.7.5 ANOVA

B.7.6 Comparative Statistics

B.8 Grouping Tools

B.8.1 Clustering

B.8.2 Association Rules

B.8.3 Decision Trees

B.9 Models Tools

B.9.1 Linear Regression

B.9.2 Logistic Regression

B.9.3 k-Nearest Neighbor

B.9.4 CART

B.10 Apply Model

B.11 Exercises

B.11.1 Overview

B.11.2 Exercise 1: Analysis of Recycling Data

B.11.3 Exercise 2: Analysis of Gold Deposit Data

B.11.4 Exercise 3: Analysis of Morphologic Difference across the Iris Plant Species

B.11.5 Exercise 4: Analysis of Census Data

Bibliography

Index

EULA

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