Analytics in a Big Data World - The Essential Guide to Data Science and its Applications

The guide to targeting and leveraging business opportunities using big data & analytics

By leveraging big data & analytics, businesses create the potential to better understand, manage, and strategically exploiting the complex dynamics of customer behavior. Analytics in a Big Data World reveals how to tap into the powerful tool of data analytics to create a strategic advantage and identify new business opportunities. Designed to be an accessible resource, this essential book does not include exhaustive coverage of all analytical techniques, instead focusing on analytics techniques that really provide added value in business environments.

The book draws on author Bart Baesens' expertise on the topics of big data, analytics and its applications in e.g. credit risk, marketing, and fraud to provide a clear roadmap for organizations that want to use data analytics to their advantage, but need a good starting point. Baesens has conducted extensive research on big data, a nalytics, customer relationship management, web analytics, fraud detection, and credit risk management, and uses this experience to bring clarity to a complex topic.
_ Includes numerous case studies on risk management, fraud detection, customer relationship management, and web analytics
_ Offers the results of research and the author's personal experience in banking, retail, and government
_ Contains an overview of the visionary ideas and current developments on the strategic use of analytics for business
_ Covers the topic of data analytics in easy-to-understand terms without an undo emphasis on mathematics and the minutiae of statistical analysis

For organizations looking to enhance their capabilities via data analytics, this resource is the go-to reference for leveraging data to enhance business capabilities.

Inhaltsverzeichnis:

Preface xiii

Acknowledgments xv

Chapter 1 Big Data and Analytics 1

Example Applications 2

Basic Nomenclature 4

Analytics Process Model 4

Job Profiles Involved 6

Analytics 7

Analytical Model Requirements 9

Notes 10

Chapter 2 Data Collection, Sampling, and Preprocessing 13

Types of Data Sources 13

Sampling 15

Types of Data Elements 17

Visual Data Exploration and Exploratory Statistical Analysis 17

Missing Values 19

Outlier Detection and Treatment 20

Standardizing Data 24

Categorization 24

Weights of Evidence Coding 28

Variable Selection 29

Segmentation 32

Notes 33

Chapter 3 Predictive Analytics 35

Target Definition 35

Linear Regression 38

Logistic Regression 39

Decision Trees 42

Neural Networks 48

Support Vector Machines 58

Ensemble Methods 64
Multiclass Classification Techniques 67

Evaluating Predictive Models 71

Notes 84

Chapter 4 Descriptive Analytics 87

Association Rules 87

Sequence Rules 94

Segmentation 95

Notes 104

Chapter 5 Survival Analysis 105

Survival Analysis Measurements 106

Kaplan Meier Analysis 109

Parametric Survival Analysis 111

Proportional Hazards Regression 114

Extensions of Survival Analysis Models 116

Evaluating Survival Analysis Models 117

Notes 117

Chapter 6 Social Network Analytics 119

Social Network Definitions 119

Social Network Metrics 121

Social Network Learning 123

Relational Neighbor Classifier 124

Probabilistic Relational Neighbor Classifier 125

Relational Logistic Regression 126

Collective Inferencing 128

Egonets 129

Bigraphs 130

Notes 132

Chapter 7 Analytics: Putting It All to Work 133

Backtesting Analytical Models 134

Benchmarking 146

Data Quality 149

Software 153

Privacy 155

Model Design and Documentation 158

Corporate Governance 159

Notes 159

Chapter 8 Example Applications 161

Credit Risk Modeling 161

Fraud Detection 165

Net Lift Response Modeling 168

Churn Prediction 172

Recommender Systems 176

Web Analytics 185

Social Media Analytics 195

Business Process Analytics 204

Notes 220

About the Author 223

Index 225