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The Resource Big data mining and machine learning : value creation for business leaders and practitioners, Jared Dean

Big data mining and machine learning : value creation for business leaders and practitioners, Jared Dean

Label
Big data mining and machine learning : value creation for business leaders and practitioners
Title
Big data mining and machine learning
Title remainder
value creation for business leaders and practitioners
Statement of responsibility
Jared Dean
Creator
Subject
Language
eng
Summary
Praise for Big Data, Data Mining, and Machine Learning: Value Creation for Business Leaders and Practitioners We needed this book, an efficient tour guide through the methods and tools of predictive modeling by an expert in the field. There are lots of books that are collections of journalistic success stories in business analytics. There are lots of books that go into the methods of predictive analytics in math<U+0127> �speak. Here we have the high<U+0127> �level tour, but with enough description to understand the guts of each method. John Sall, Executive Vice President, SAS Institute Jared Dean provides an interesting and approachable perspective on one of todays most discussed topics: using big data and analytics to create value for organizations. The combination of simple examples and deep insights make this a vital read for managers who need to have a complete picture of the analytical process and the great potential it unlocks. Chris Bingham, Philip Hettleman Scholar and Associate Professor of Strategy Entrepreneurship, The University of North Carolina at Chapel Hill This book provides excellent coverage of the technical skills needed by analytical consultants in todays market. The focus on modern methods makes this book relevant for business leaders who want to reap the rewards that analytics can bring to an organization. In my experience, one of the greatest missing links in implementing an analytics<U+0127> �based strategy is a shortage of executives who truly understand analyticsboth the capabilities analytics can provide as well as the limitations. This book can help you close that knowledge gap. Jared does an excellent job of making the concepts approachable while giving complete explanations with ample examples. Mark Pitts, MS, MAcc, Vice President, Enterprise Informatics Data Analytics, Highmark Health A wonderful treatise that cuts through the noise about big data and lays out clearly what it is, how it can be integrated with data analytic models, and how companies can leverage it to add value to their business. I am confident this book will be a must read for anyone trying to make sense of how to convert big data into actionable insights for their organization. Dr. Goutam Chakraborty, Professor (Marketing) and Director of Graduate Certificate in Business Data Mining, Oklahoma State University .--
Member of
Assigning source
Provided by publisher
Cataloging source
DLC
http://library.link/vocab/creatorDate
1978-
http://library.link/vocab/creatorName
Dean, Jared
Dewey number
658.05631
Index
index present
Literary form
non fiction
Series statement
Wiley & SAS business series
http://library.link/vocab/subjectName
  • Management
  • Data mining
  • Big data
  • Database management
  • Information technology
Label
Big data mining and machine learning : value creation for business leaders and practitioners, Jared Dean
Instantiates
Publication
Note
Includes index
Carrier category
volume
Carrier MARC source
rdacarrier.
Content category
text
Content type MARC source
rdacontent.
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 Classification 64 Multilevel Classification 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 Classification 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 Profiling 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
Control code
FIEb17603390
Dimensions
25 cm.
Extent
xix, 265 pages
Isbn
9781502462916
Media category
unmediated
Media MARC source
rdamedia.
System control number
(OCoLC)914479116
Label
Big data mining and machine learning : value creation for business leaders and practitioners, Jared Dean
Publication
Note
Includes index
Carrier category
volume
Carrier MARC source
rdacarrier.
Content category
text
Content type MARC source
rdacontent.
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 Classification 64 Multilevel Classification 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 Classification 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 Profiling 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
Control code
FIEb17603390
Dimensions
25 cm.
Extent
xix, 265 pages
Isbn
9781502462916
Media category
unmediated
Media MARC source
rdamedia.
System control number
(OCoLC)914479116

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