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Produktbild: Practical Data Science with Hadoop and Spark: Designing and Building Effective Analytics at Scale

Practical Data Science with Hadoop and Spark: Designing and Building Effective Analytics at Scale Designing and Building Effective Analytics at Scale

Fr. 47.90

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Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

12.12.2016

Verlag

Pearson Education Limited

Seitenzahl

256

Maße (L/B/H)

23.2/17.8/1.5 cm

Gewicht

363 g

Auflage

1

Sprache

Englisch

ISBN

978-0-13-402414-1

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

12.12.2016

Verlag

Pearson Education Limited

Seitenzahl

256

Maße (L/B/H)

23.2/17.8/1.5 cm

Gewicht

363 g

Auflage

1

Sprache

Englisch

ISBN

978-0-13-402414-1

Herstelleradresse


Email: info@bod.de

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  • Produktbild: Practical Data Science with Hadoop and Spark: Designing and Building Effective Analytics at Scale

  • Foreword xiii

    Preface xv

    Acknowledgments xxi

    About the Authors xxiii

    Part I: Data Science with Hadoop—An Overview 1

    Chapter 1: Introduction to Data Science 3

    What Is Data Science? 3

    Example: Search Advertising 4

    A Bit of Data Science History 5

    Becoming a Data Scientist 8

    Building a Data Science Team 12

    The Data Science Project Life Cycle 13

    Managing a Data Science Project 18

    Summary 18

    Chapter 2: Use Cases for Data Science 19

    Big Data—A Driver of Change 19

    Business Use Cases 21

    Summary 29

    Chapter 3: Hadoop and Data Science 31

    What Is Hadoop? 31

    Hadoop’s Evolution 37

    Hadoop Tools for Data Science 38

    Why Hadoop Is Useful to Data Scientists 46

    Summary 51

    Part II: Preparing and Visualizing Data with Hadoop 53

    Chapter 4: Getting Data into Hadoop 55

    Hadoop as a Data Lake 56

    The Hadoop Distributed File System (HDFS) 58

    Direct File Transfer to Hadoop HDFS 58

    Importing Data from Files into Hive Tables 59

    Importing Data into Hive Tables Using Spark 62

    Using Apache Sqoop to Acquire Relational Data 65

    Using Apache Flume to Acquire Data Streams 74

    Manage Hadoop Work and Data Flows with Apache

    Oozie 79

    Apache Falcon 81

    What’s Next in Data Ingestion? 82

    Summary 82

    Chapter 5: Data Munging with Hadoop 85

    Why Hadoop for Data Munging? 86

    Data Quality 86

    The Feature Matrix 93

    Summary 106

    Chapter 6: Exploring and Visualizing Data 107

    Why Visualize Data? 107

    Creating Visualizations 112

    Using Visualization for Data Science 121

    Popular Visualization Tools 121

    Visualizing Big Data with Hadoop 123

    Summary 124

    Part III: Applying Data Modeling with Hadoop 125

    Chapter 7: Machine Learning with Hadoop 127

    Overview of Machine Learning 127

    Terminology 128

    Task Types in Machine Learning 129

    Big Data and Machine Learning 130

    Tools for Machine Learning 131

    The Future of Machine Learning and Artificial Intelligence 132

    Summary 132

    Chapter 8: Predictive Modeling 133

    Overview of Predictive Modeling 133

    Classification Versus Regression 134

    Evaluating Predictive Models 136

    Supervised Learning Algorithms 140

    Building Big Data Predictive Model Solutions 141

    Example: Sentiment Analysis 145

    Summary 150

    Chapter 9: Clustering 151

    Overview of Clustering 151

    Uses of Clustering 152

    Designing a Similarity Measure 153

    Clustering Algorithms 154

    Example: Clustering Algorithms 155

    Evaluating the Clusters and Choosing the Number of Clusters 157

    Building Big Data Clustering Solutions 158

    Example: Topic Modeling with Latent Dirichlet Allocation 160

    Summary 163

    Chapter 10: Anomaly Detection with Hadoop 165

    Overview 165

    Uses of Anomaly Detection 166

    Types of Anomalies in Data 166

    Approaches to Anomaly Detection 167

    Tuning Anomaly Detection Systems 170

    Building a Big Data Anomaly Detection Solution with Hadoop 171

    Example: Detecting Network Intrusions 172

    Summary 179

    Chapter 11: Natural Language Processing 181

    Natural Language Processing 181

    Tooling for NLP in Hadoop 184

    Textual Representations 187

    Sentiment Analysis Example 189

    Summary 193

    Chapter 12: Data Science with Hadoop—The Next Frontier 195

    Automated Data Discovery 195

    Deep Learning 197

    Summary 199

    Appendix A: Book Web Page and Code Download 201

    Appendix B: HDFS Quick Start 203

    Quick Command Dereference 204

    Appendix C: Additional Background on Data Science and Apache Hadoop and Spark 209

    General Hadoop/Spark Information 209

    Hadoop/Spark Installation Recipes 210

    HDFS 210

    MapReduce 211

    Spark 211

    Essential Tools 211

    Machine Learning 212

    Index 213