Details

Analytics in a Big Data World


Analytics in a Big Data World

The Essential Guide to Data Science and its Applications
Wiley and SAS Business Series 1. Aufl.

von: Bart Baesens

33,99 €

Verlag: Wiley
Format: EPUB
Veröffentl.: 15.04.2014
ISBN/EAN: 9781118892749
Sprache: englisch
Anzahl Seiten: 256

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Beschreibungen

<b>The guide to targeting and leveraging business opportunities using big data & analytics</b> <p>By leveraging big data & analytics, businesses create the potential to better understand, manage, and strategically exploiting the complex dynamics of customer behavior. <i>Analytics in a Big Data World</i> 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.</p> <p>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.</p> <ul> <li>Includes numerous case studies on risk management, fraud detection, customer relationship management, and web analytics</li> <li>Offers the results of research and the author's personal experience in banking, retail, and government</li> <li>Contains an overview of the visionary ideas and current developments on the strategic use of analytics for business</li> <li>Covers the topic of data analytics in easy-to-understand terms without an undo emphasis on mathematics and the minutiae of statistical analysis</li> </ul> <p>For organizations looking to enhance their capabilities via data analytics, this resource is the go-to reference for leveraging data to enhance business capabilities.</p>
<p>Preface xiii</p> <p>Acknowledgments xv</p> <p><b>Chapter 1 Big Data and Analytics 1</b></p> <p>Example Applications 2</p> <p>Basic Nomenclature 4</p> <p>Analytics Process Model 4</p> <p>Job Profiles Involved 6</p> <p>Analytics 7</p> <p>Analytical Model Requirements 9</p> <p>Notes 10</p> <p><b>Chapter 2 Data Collection, Sampling, and Preprocessing 13</b></p> <p>Types of Data Sources 13</p> <p>Sampling 15</p> <p>Types of Data Elements 17</p> <p>Visual Data Exploration and Exploratory Statistical Analysis 17</p> <p>Missing Values 19</p> <p>Outlier Detection and Treatment 20</p> <p>Standardizing Data 24</p> <p>Categorization 24</p> <p>Weights of Evidence Coding 28</p> <p>Variable Selection 29</p> <p>Segmentation 32</p> <p>Notes 33</p> <p><b>Chapter 3 Predictive Analytics 35</b></p> <p>Target Definition 35</p> <p>Linear Regression 38</p> <p>Logistic Regression 39</p> <p>Decision Trees 42</p> <p>Neural Networks 48</p> <p>Support Vector Machines 58</p> <p>Ensemble Methods 64</p> <p>Multiclass Classification Techniques 67</p> <p>Evaluating Predictive Models 71</p> <p>Notes 84</p> <p><b>Chapter 4 Descriptive Analytics 87</b></p> <p>Association Rules 87</p> <p>Sequence Rules 94</p> <p>Segmentation 95</p> <p>Notes 104</p> <p><b>Chapter 5 Survival Analysis 105</b></p> <p>Survival Analysis Measurements 106</p> <p>Kaplan Meier Analysis 109</p> <p>Parametric Survival Analysis 111</p> <p>Proportional Hazards Regression 114</p> <p>Extensions of Survival Analysis Models 116</p> <p>Evaluating Survival Analysis Models 117</p> <p>Notes 117</p> <p><b>Chapter 6 Social Network Analytics 119</b></p> <p>Social Network Definitions 119</p> <p>Social Network Metrics 121</p> <p>Social Network Learning 123</p> <p>Relational Neighbor Classifier 124</p> <p>Probabilistic Relational Neighbor Classifier 125</p> <p>Relational Logistic Regression 126</p> <p>Collective Inferencing 128</p> <p>Egonets 129</p> <p>Bigraphs 130</p> <p>Notes 132</p> <p><b>Chapter 7 Analytics: Putting It All to Work 133</b></p> <p>Backtesting Analytical Models 134</p> <p>Benchmarking 146</p> <p>Data Quality 149</p> <p>Software 153</p> <p>Privacy 155</p> <p>Model Design and Documentation 158</p> <p>Corporate Governance 159</p> <p>Notes 159</p> <p><b>Chapter 8 Example Applications 161</b></p> <p>Credit Risk Modeling 161</p> <p>Fraud Detection 165</p> <p>Net Lift Response Modeling 168</p> <p>Churn Prediction 172</p> <p>Recommender Systems 176</p> <p>Web Analytics 185</p> <p>Social Media Analytics 195</p> <p>Business Process Analytics 204</p> <p>Notes 220</p> <p>About the Author 223</p> <p>Index 225</p>
<p><b>BART BAESENS</b> 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 <i>Machine Learning</i> and <i>Management Science</i>. Baesens is also co-author of the book <i>Credit Risk Management: Basic Concepts</i> (Oxford University Press, 2008).
<p>A few years ago, big data was little more than a buzzword. Today, it's a reality for every business, but only a few firms are taking advantage of the new world of information. The science of analytics is a way to get inside customers' minds and understand the complex behavioral dynamics that affect business. <i>Analytics in a Big Data World</i> advances the discussion of big data by moving it out of the theoretical realm and into everyday business practice. <p>It has been said that data is the new oil—an abundant resource of great value. The difference between data and oil, as top analytics researcher Bart Baesens understands, is that <i>everyone</i> has data. In areas like risk management, fraud detection, and customer relationship management, the potential gains afforded by big data analytics are well worth exploring. Reading <i>Analytics in a Big Data</i> <i>World</i> is the first step in extracting the valuable information waiting in your databases. <p>By taking a practitioner's perspective, this book shows readers how to use the latest developments and new ideas in big data to build an analytics strategy with practical applications. The mathematics and theory have already been tested, so <i>Analytics in a Big Data World</i> draws on case studies and action plans, rather than dwelling unnecessarily on technical details. This realistic focus makes the guide ideal for analytics professionals who want to learn the latest techniques for leveraging data to expand markets. <p>This latest addition to the Wiley and SAS Business Series is relevant to decisions that all businesses will need to make in the coming years. As the number of practical applications for data skyrockets, learning how to extract business value from big data becomes a competitive requirement. Bart Baesens has accomplished something significant with <i>Analytics in a Big Data World</i>, which delivers an action-oriented guide to staying competitive using the latest analytical models.