Big Data, Data Mining, and Machine Learning

Value Creation for Business Leaders and Practitioners

AvJared Dean

Inbunden, Engelska, 2014

Del i serien Wiley and SAS Business Series

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Beskrivning

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 characteristicsDetails on high performance computing architectures for analytics, massively parallel processing (MPP), and in-memory databasesComprehensive coverage of data mining, text analytics, and machine learning algorithmsA discussion of explanatory and predictive modeling, and how they can be applied to decision-making processesBig 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.

Produktinformation

Utgivningsdatum:2014-08-08
Mått:163 x 236 x 31 mm
Vikt:499 g
Format:Inbunden
Språk:Engelska
Serie:Wiley and SAS Business Series
Antal sidor:288
Förlag:John Wiley & Sons Inc
ISBN:9781118618042

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Mer om författaren

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.

Recensioner i media

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

Innehållsförteckning

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