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 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.
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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.
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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
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In today's business environment an endless stream of big data often shapes critical decision-making processes. To maintain and sustain a profitable business, it is imperative to harness the power of big data. However, simply accessing the data and having the ability to process it isn't enough to yield meaningful results. Big Data, Data Mining, and Machine Learning offers marketing executives, business leaders, and technology experts a comprehensive resource for developing and implementing the strategies and methods that can consistently produce effective results and ultimately increase profitability. In this book, Jared Dean offers an accessible and thorough review of the current state of big data analytics and the growing trend toward high performance computing architectures. Big Data, Data Mining, and Machine Learning clearly shows how big data analytics can be leveraged to foster positive change and drive efficiency. Step by step, Jared Dean reveals what it takes to use technology to create an analytical environment for data mining, machine learning, and working with big data. The author also explores the trade-offs that result from certain technology choices. Big Data, Data Mining, and Machine Learning includes a range of algorithms and methods that can be implemented to glean information from mined data and provides explanations on how to apply these approaches most effectively. Filled with illustrative case studies, the book offers myriad examples of successful organizations that have used new technological advances and algorithms to their competitive advantage. The author also includes a discussion of explanatory and predictive modeling and how these tools can be applied to the decision-making process. For any organization that wants to access the power of data analytics, this important book can serve as a linchpin for understanding the underlying technology and analysis of big data. Now you can take control of your organization's big data analytics with confidence and create results that go directly to the bottom line.
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explains what it covers very well (ZDNet, September 2014)

Produktdetaljer

ISBN
9781118618042
Publisert
2014-08-08
Utgiver
Vendor
John Wiley & Sons Inc
Vekt
499 gr
Høyde
236 mm
Bredde
163 mm
Dybde
31 mm
Aldersnivå
P, 06
Språk
Product language
Engelsk
Format
Product format
Innbundet
Antall sider
288

Forfatter

Biographical note

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.