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.comA 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 dataMaking 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.

Preface ix1 Introduction 11.1 Overview 11.2 Sources of Data 21.3 Process for Making Sense of Data 31.4 Overview of Book 131.5 Summary 16Further Reading 162 Describing Data 172.1 Overview 172.2 Observations and Variables 182.3 Types of Variables 202.4 Central Tendency 222.5 Distribution of the Data 242.6 Confidence Intervals 362.7 Hypothesis Tests 40Exercises 42Further Reading 453 Preparing Data Tables 473.1 Overview 473.2 Cleaning the Data 483.3 Removing Observations and Variables 493.4 Generating Consistent Scales Across Variables 493.5 New Frequency Distribution 513.6 Converting Text to Numbers 523.7 Converting Continuous Data to Categories 533.8 Combining Variables 543.9 Generating Groups 543.10 Preparing Unstructured Data 55Exercises 57Further Reading 574 Understanding Relationships 594.1 Overview 594.2 Visualizing Relationships Between Variables 604.3 Calculating Metrics About Relationships 69Exercises 81Further Reading 825 Identifying and Understanding Groups 835.1 Overview 835.2 Clustering 885.3 Association Rules 1115.4 Learning Decision Trees from Data 122Exercises 137Further Reading 1406 Building Models From Data 1416.1 Overview 1416.2 Linear Regression 1496.3 Logistic Regression 1616.4 k-Nearest Neighbors 1676.5 Classification and Regression Trees 1726.6 Other Approaches 178Exercises 179Further Reading 182Appendix A Answers to Exercises 185Appendix B Hands-on Tutorials 191B. 1 Tutorial Overview 191B. 2 Access and Installation 191B. 3 Software Overview 192B. 4 Reading in Data 193B. 5 Preparation Tools 195B. 6 Tables and Graph Tools 199B. 7 Statistics Tools 202B. 8 Grouping Tools 204B. 9 Models Tools 207B. 10 Apply Model 211B. 11 Exercises 211Bibliography 227Index 231