With big data analytics comes big insights into profitability Big data is big business. But having the data and the computational power to process it isn't nearly enough to produce meaningful results. Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners is a complete resource for technology and marketing executives looking to cut through the hype and produce real results that hit the bottom line. Providing an engaging, thorough overview of the current state of big data analytics and the growing trend toward high performance computing architectures, the book is a detail-driven look into how big data analytics can be leveraged to foster positive change and drive efficiency. With continued exponential growth in data and ever more competitive markets, businesses must adapt quickly to gain every competitive advantage available. Big data analytics can serve as the linchpin for initiatives that drive business, but only if the underlying technology and analysis is fully understood and appreciated by engaged stakeholders. This book provides a view into the topic that executives, managers, and practitioners require, and includes: A complete overview of big data and its notable characteristics Details on high performance computing architectures for analytics, massively parallel processing (MPP), and in-memory databases Comprehensive coverage of data mining, text analytics, and machine learning algorithms A discussion of explanatory and predictive modeling, and how they can be applied to decision-making processes Big Data, Data Mining, and Machine Learning provides technology and marketing executives with the complete resource that has been notably absent from the veritable libraries of published books on the topic. Take control of your organization's big data analytics to produce real results with a resource that is comprehensive in scope and light on hyperbole.

Table of Contents:
Forward xiii Preface xv Acknowledgments xix Introduction 1 Big Data Timeline 5 Why This Topic is Relevant Now 8 Is Big Data a Fad? 9 Where Using Big Data Makes a Big Difference 12 Part One The Computing Environment 23 Chapter 1 Hardware 27 Storage (Disk) 27 Central Processing Unit 29 Memory 31 Network 33 Chapter 2 Distributed Systems 35 Database Computing 36 File System Computing 37 Considerations 39 Chapter 3 Analytical Tools 43 Weka 43 Java and JVM Languages 44 R 47 Python 49 SAS 50 Part Two Turning Data into Business Value 53 Chapter 4 Predictive Modeling 55 A Methodology for Building Models 58 sEMMA 61 Binary Classifi cation 64 Multilevel Classifi cation 66 Interval Prediction 66 Assessment of Predictive Models 67 Chapter 5 Common Predictive Modeling Techniques 71 RFM 72 Regression 75 Generalized Linear Models 84 Neural Networks 90 Decision and Regression Trees 101 Support Vector Machines 107 Bayesian Methods Network Classifi cation 113 Ensemble Methods 124 Chapter 6 Segmentation 127 Cluster Analysis 132 Distance Measures (Metrics) 133 Evaluating Clustering 134 Number of Clusters 135 K‐means Algorithm 137 Hierarchical Clustering 138 Profi ling Clusters 138 Chapter 7 Incremental Response Modeling 141 Building the Response Model 142 Measuring the Incremental Response 143 Chapter 8 Time Series Data Mining 149 Reducing Dimensionality 150 Detecting Patterns 151 Time Series Data Mining in Action: Nike+ FuelBand 154 Chapter 9 Recommendation Systems 163 What Are Recommendation Systems? 163 Where Are They Used? 164 How Do They Work? 165 Assessing Recommendation Quality 170 Recommendations in Action: SAS Library 171 Chapter 10 Text Analytics 175 Information Retrieval 176 Content Categorization 177 Text Mining 178 Text Analytics in Action: Let’s Play Jeopardy! 180 Part Three Success Stories of Putting It All Together 193 Chapter 11 Case Study of a Large U.S.‐Based Financial Services Company 197 Traditional Marketing Campaign Process 198 High‐Performance Marketing Solution 202 Value Proposition for Change 203 Chapter 12 Case Study of a Major Health Care Provider 205 CAHPS 207 HEDIS 207 HOS 208 IRE 208 Chapter 13 Case Study of a Technology Manufacturer 215 Finding Defective Devices 215 How They Reduced Cost 216 Chapter 14 Case Study of Online Brand Management 221 Chapter 15 Case Study of Mobile Application Recommendations 225 Chapter 16 Case Study of a High‐Tech Product Manufacturer 229 Handling the Missing Data 230 Application beyond Manufacturing 231 Chapter 17 Looking to the Future 233 Reproducible Research 234 Privacy with Public Data Sets 234 The Internet of Things 236 Software Development in the Future 237 Future Development of Algorithms 238 In Conclusion 241 About the Author 243 Appendix 245 References 247 Index 253