Analytics in a Big Data World: The Essential Guide to Data Science and its Applications (Wiley and SAS Business Series)
By: Bart Baesens (author)Hardback
1 - 2 weeks availability
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, analytics, 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.
BART BAESENS is an associate professor at KU Leuven (Belgium) and a lecturer at the University of Southampton (United Kingdom), as well as an internationally known data analytics consultant. He is a foremost researcher in the areas of web analytics, customer relationship management, and fraud detection. His findings have been published in well-known international journals including Machine Learning and Management Science. Baesens is also co-author of the book Credit Risk Management: Basic Concepts (Oxford University Press, 2008).
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
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