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Index
Cover Title Page Copyright Page Book Series
Mission Coverage
Editorial Advisory Board and List of Reviewers
List of Reviewers
Preface
INTRODUCTION MOTIVATION ORGANIZATION OF THE BOOK REFERENCES
Acknowledgment Chapter 1: What Is Open Source Software (OSS) and What Is Big Data?
ABSTRACT INTRODUCTION: HOW OPEN SOURCE SOFTWARE, FREE SOFTWARE, AND FREEWARE DIFFER INTRODUCTION: WHAT IS BIG DATA? CHARACTERISTICS OF BIG DATA THE INTERFACE OF COMPONENTS OF BIG DATA STACK OPEN SOURCE SOFTWARE PLATFORMS FOR CLOUD AND FOG COMPUTING BIG DATA ANALYTICS SOFTWARE AND PLATFORMS BIG DATA VISUALIZATION BIG DATA VISUALIZATION SOFTWARE BIG DATA VISUALIZATION FOR STREAMING DATA BIG DATA VISUALIZATION SOFTWARE FOR MOBILE DEVICES AND TABLETS CONCLUSION ACKNOWLEDGMENT REFERENCES KEY TERMS AND DEFINITIONS APPENDIX
Chapter 2: Open Source Software (OSS) for Big Data
ABSTRACT OPEN SOURCE SOFTWARE AND TECHNOLOGY FOR BIG DATA OPEN SOURCE HADOOP-RELATED PROJECTS BIG DATA SMACK OPPORTUNITIES FOR BIG DATA AND OPEN SOURCE SOFTWARE STATISTICAL OPEN SOURCE SOFTWARE (OSS) FOR BIG DATA CHALLENGES FOR BIG DATA AND OPEN SOURCE SOFTWARE (OSS) CONCLUSION ACKNOWLEDGMENT REFERENCES KEY TERMS AND DEFINITIONS
Chapter 3: Introduction to the Popular Open Source Statistical Software (OSSS)
ABSTRACT INTRODUCTION BACKGROUND PYTHON GRETL (GNU REGRESSION, ECONOMETRICS, AND TIME-SERIES LIBRARY) SOFA (STATISTICS OPEN FOR ALL) STATISTICS OCTAVE KNIME (KONSTANZ INFORMATION MINER) SCILAB CONCLUSION REFERENCES KEY TERMS AND DEFINITIONS
Chapter 4: Cluster Analysis in R With Big Data Applications
ABSTRACT INTRODUCTION BACKGROUND CLUSTERING WITH RNA-SEQUENCING DATA FUTURE RESEARCH DIRECTIONS CONCLUSION REFERENCES ADDITIONAL READING KEY TERMS AND DEFINITIONS
Chapter 5: Generalized Linear Model for Automobile Fatality Rate Prediction in R
ABSTRACT INTRODUCTION BACKGROUND R WORKING ENVIRONMENT DESCRIPTIVE ANALYSIS MODEL CONSTRUCTION MODEL VALIDATION CONCLUSION REFERENCES KEY TERMS AND DEFINITIONS
Chapter 6: Introduction to Python and Its Statistical Applications
ABSTRACT BACKGROUND: HISTORY OF PYTHON IDEs AND CODE EDITORS PYTHON LIBRARIES FOR STATISTICAL ANALYSIS APPLICATION: COLLISION DATA ANALYSIS CONCLUSION REFERENCES KEY TERMS AND DEFINITIONS APPENDIX 1
Chapter 7: A Comparison of Machine Learning Algorithms of Big Data for Time Series Forecasting Using Python
ABSTRACT INTRODUCTION BACKGROUND AND LITERATURE REVIEW DATA EXPLORATION AND PRE-PROCESSING BASE MODELS MACHINE LEARNING MODELS EVALUATION METRICS BASE MODEL EVALUATION MACHINE LEARNING MODEL EVALUATION CONCLUSION REFERENCES KEY TERMS AND DEFINITIONS
Conclusion Related Readings About the Contributors
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