Data Mining for Business Analytics

Galit Shmueli , Peter C. Bruce , Mia L. Stephens , Nitin R. Patel 지음

Wiley 2016년 05월 11일 출간

작품소개

책소개

Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro？ presents an applied and interactive approach to data mining. Featuring hands-on applications with JMP Pro？, a statistical package from the SAS Institute, the bookuses engaging, real-world examples to build a theoretical and practical understanding of key data mining methods, especially predictive models for classification and prediction. Topics include data visualization, dimension reduction techniques, clustering, linear and logistic regression, classification and regression trees, discriminant analysis, naive Bayes, neural networks, uplift modeling, ensemble models, and time series forecasting. Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro？ also includes: Detailed summaries that supply an outline of key topics at the beginning of each chapter End-of-chapter examples and exercises that allow readers to expand their comprehension of the presented material Data-rich case studies to illustrate various applications of data mining techniques A companion website with over two dozen data sets, exercises and case study solutions, and slides for instructors Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro？ is an excellent textbook for advanced undergraduate and graduate-level courses on data mining, predictive analytics, and business analytics. The book is also a one-of-a-kind resource for data scientists, analysts, researchers, and practitioners working with analytics in the fields of management, finance, marketing, information technology, healthcare, education, and any other data-rich field. Galit Shmueli, PhD, is Distinguished Professor at National Tsing Hua University’s Institute of Service Science. She has designed and instructed data mining courses since 2004 at University of Maryland, Statistics.

목차

Dedication i Foreword xvii Preface xviii Acknowledgments xx PART I PRELIMINARIES CHAPTER 1 Introduction 3 1.1 What is Business Analytics? 3 1.2 What is Data Mining? 5 1.3 Data Mining and Related Terms 5 1.4 Big Data 6 1.5 Data science 7 1.6 Why Are There So Many Different Methods? 8 1.7 Terminology and Notation 9 1.8 Road Maps to This Book 11 Order of Topics 12 CHAPTER 2 Overview of the Data Mining Process 15 2.1 Introduction 15 2.2 Core Ideas in Data Mining 16 2.3 The Steps in Data Mining 19 2.4 Preliminary Steps 20 2.5 Predictive Power and Overfitting 28 2.6 Building a Predictive Model with JMP Pro 33 2.7 Using JMP Pro for Data Mining 42 2.8 Automating Data Mining Solutions 42 Data Mining Software Tools (Herb Edelstein) 44 Problems 47 PART II DATA EXPLORATION AND DIMENSION REDUCTION CHAPTER 3 Data Visualization 52 3.1 Uses of Data Visualization 52 3.2 Data Examples 54 Example 1: Boston Housing Data 54 Example 2: Ridership on Amtrak Trains 55 3.3 Basic Charts: Bar Charts, Line Graphs, and Scatterplots 55 Distribution Plots 58 Heatmaps: visualizing correlations and missing values 61 3.4 Multi-Dimensional Visualization 63 Adding Variables: Color, Hue, Size, Shape, Multiple Panels, Animation 63 Manipulations: Re-scaling, Aggregation and Hierarchies, Zooming and Panning, Filtering 67 Reference: Trend Line and Labels 70 Scaling Up: Large Datasets 72 Multivariate Plot: Parallel Coordinates Plot 73 Interactive Visualization 74 3.5 Specialized Visualizations 76 Visualizing Networked Data 76 Visualizing Hierarchical Data: Tr

작가정보

저자(글) Galit Shmueli

저자(글) Peter C. Bruce

저자(글) Mia L. Stephens

저자(글) Nitin R. Patel