Making Sense of Data I
A Practical Guide to Exploratory Data Analysis and Data Mining
2nd Edition
Published on 2. July 2014
248 pages
978-1-118-42201-4 (ISBN)
Description
Praise for the First Edition
"...a well-written book on data analysis anddata mining that provides an excellent foundation..."
--CHOICE
"This is a must-read book for learning practicalstatistics and data analysis..."
--Computing Reviews.com
A proven go-to guide for data analysis, Making Sense of DataI: A Practical Guide to Exploratory Data Analysis and Data Mining,Second Edition focuses on basic data analysis approaches thatare necessary to make timely and accurate decisions in a diverserange of projects. Based on the authors' practical experiencein implementing data analysis and data mining, the new editionprovides clear explanations that guide readers from almost everyfield of study.In order to facilitate the needed steps when handling a dataanalysis or data mining project, a step-by-step approach aidsprofessionals in carefully analyzing data and implementing results,leading to the development of smarter business decisions. The toolsto summarize and interpret data in order to master data analysisare integrated throughout, and the Second Edition alsofeatures:
* Updated exercises for both manual and computer-aidedimplementation with accompanying worked examples
* New appendices with coverage on the freely availableTraceis(TM) software, including tutorials using data from avariety of disciplines such as the social sciences, engineering,and finance
* New topical coverage on multiple linear regression and logisticregression to provide a range of widely used and transparentapproaches
* Additional real-world examples of data preparation to establisha practical background for making decisions from data
Making Sense of Data I: A Practical Guide to Exploratory DataAnalysis and Data Mining, Second Edition is an excellentreference for researchers and professionals who need to achieveeffective decision making from data. The Second Edition isalso an ideal textbook for undergraduate and graduate-level coursesin data analysis and data mining and is appropriate forcross-disciplinary courses found within computer science andengineering departments.
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Additional editions
Glenn J. Myatt | Wayne P. Johnson
Making Sense of Data I
A Practical Guide to Exploratory Data Analysis and Data Mining
Book
07/2014
2nd Edition
Wiley
€78.00
Shipment within 15-20 days
Software
07/2014
Wiley
€110.80
No shipping information available
Persons
Glenn J. Myatt, PhD, is Chief Scientific Officer and Cofounder of Leadscope, Inc. The author of numerous journal articles, Dr. Myatt, is also the coauthor of Making Sense of Data II: A Practical Guide to Data Visualization, Advanced Data Mining Methods, and Applications and Making Sense of Data III: A Practical Guide to Designing Interactive Data Visualizations, both of which are published by Wiley.
Wayne P. Johnson, MSc, is Cofounder of Leadscope, Inc., as well as a partner of Myatt & Johnson, Inc. He has over 35 years of experience in software engineering related to operating systems, telecommunications, and artificial intelligence at various companies including IBM, AT&T Bell Laboratories, and Ford Motor Company. He has led research projects related to informatics, and in addition to authoring numerous journal articles, Mr. Johnson is the coauthor of Making Sense of Data II: A Practical Guide to Data Visualization, Advanced Data Mining Methods, and Applications and Making Sense of Data III: A Practical Guide to Designing Interactive Data Visualizations, both of which are published by Wiley.
Content
PREFACE ix
1 INTRODUCTION 1
1.1 Overview 1
1.2 Sources of Data 2
1.3 Process for Making Sense of Data 3
1.4 Overview of Book 13
1.5 Summary 16
Further Reading 16
2 DESCRIBING DATA 17
2.1 Overview 17
2.2 Observations and Variables 18
2.3 Types of Variables 20
2.4 Central Tendency 22
2.5 Distribution of the Data 24
2.6 Confidence Intervals 36
2.7 Hypothesis Tests 40
Exercises 42
Further Reading 45
3 PREPARING DATA TABLES 47
3.1 Overview 47
3.2 Cleaning the Data 48
3.3 Removing Observations and Variables 49
3.4 Generating Consistent Scales Across Variables 49
3.5 New Frequency Distribution 51
3.6 Converting Text to Numbers 52
3.7 Converting Continuous Data to Categories 53
3.8 Combining Variables 54
3.9 Generating Groups 54
3.10 Preparing Unstructured Data 55
Exercises 57
Further Reading 57
4 UNDERSTANDING RELATIONSHIPS 59
4.1 Overview 59
4.2 Visualizing Relationships Between Variables 60
4.3 Calculating Metrics About Relationships 69
Exercises 81
Further Reading 82
5 IDENTIFYING AND UNDERSTANDING GROUPS 83
5.1 Overview 83
5.2 Clustering 88
5.3 Association Rules 111
5.4 Learning Decision Trees from Data 122
Exercises 137
Further Reading 140
6 BUILDING MODELS FROM DATA 141
6.1 Overview 141
6.2 Linear Regression 149
6.3 Logistic Regression 161
6.4 k-Nearest Neighbors 167
6.5 Classification and Regression Trees 172
6.6 Other Approaches 178
Exercises 179
Further Reading 182
APPENDIX A ANSWERS TO EXERCISES 185
APPENDIX B HANDS-ON TUTORIALS 191
B.1 Tutorial Overview 191
B.2 Access and Installation 191
B.3 Software Overview 192
B.4 Reading in Data 193
B.5 Preparation Tools 195
B.6 Tables and Graph Tools 199
B.7 Statistics Tools 202
B.8 Grouping Tools 204
B.9 Models Tools 207
B.10 Apply Model 211
B.11 Exercises 211
BIBLIOGRAPHY 227
INDEX 231
