JARED DEAN is a Senior Director of Research and Development at SAS Institute. He is responsible for the development of SAS's worldwide data mining solutions. This includes customer engagements, new feature development, technical support, sales support, and product integration. Prior to joining SAS, Dean worked as a Mathematical Statistician for the US Census Bureau.

"...explains what it covers very well..." (ZDNet, September 2014)

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

JARED DEAN is a Senior Director of Research and Development at SAS Institute. He is responsible for the development of SAS's worldwide data mining solutions. This includes customer engagements, new feature development, technical support, sales support, and product integration. Prior to joining SAS, Dean worked as a Mathematical Statistician for the US Census Bureau.

"...explains what it covers very well..." (ZDNet, September 2014)

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

JARED DEAN is a Senior Director of Research and Development at SAS Institute. He is responsible for the development of SAS's worldwide data mining solutions. This includes customer engagements, new feature development, technical support, sales support, and product integration. Prior to joining SAS, Dean worked as a Mathematical Statistician for the US Census Bureau.

"...explains what it covers very well..." (ZDNet, September 2014)

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