Successfully navigating the data-driven economy presupposes a certain understanding of the technologies and methods to gain insights from Big Data. This book aims to help data science practitioners to successfully manage the transition to Big Data.
Successfully navigating the data-driven economy presupposes a certain understanding of the technologies and methods to gain insights from Big Data. This book aims to help data science practitioners to successfully manage the transition to Big Data.
Ulrich Matter is an Assistant Professor of Economics at the University of St.Gallen. His primary research interests lie at the intersection of data science, political economics, and media economics. His teaching activities cover topics in data science, applied econometrics, and data analytics. Before joining the University of St. Gallen, he was a Visiting Researcher at the Berkman Klein Center for Internet & Society at Harvard University and a postdoctoral researcher and lecturer at the Faculty for Business and Economics, University of Basel.
Inhaltsangabe
Part 1. Setting the Scene: Analyzing Big Data 1. What is Big in "Big Data"? 2. Approaches to Analyzing Big Data 3. The Two Domains of Big Data Analytics Part 2. Platform: Software and Computing Resources 4. Software: Programming with (Big) Data 5. Hardware: Computing Resources 6. Distributed Systems 7. Cloud Computing Part 3. Components of Big Data Analytics 8. Data Collection and Data Storage 9. Big Data Cleaning and Transformation 10. Descriptive Statistics and Aggregation 11. (Big) Data Visualization Part 4. Application: Topics in Big Data Econometrics 12. Bottlenecks in Everyday Data Analytics Tasks 13. Econometrics with GPUs 14. Regression Analysis and Categorization with Spark and R 15. Large-scale Text Analysis with sparklyr Part 5. Appendices Appendix A. GitHub Appendix B. R Basics Appendix C. Install Hadoop
Part 1. Setting the Scene: Analyzing Big Data 1. What is Big in "Big Data"? 2. Approaches to Analyzing Big Data 3. The Two Domains of Big Data Analytics Part 2. Platform: Software and Computing Resources 4. Software: Programming with (Big) Data 5. Hardware: Computing Resources 6. Distributed Systems 7. Cloud Computing Part 3. Components of Big Data Analytics 8. Data Collection and Data Storage 9. Big Data Cleaning and Transformation 10. Descriptive Statistics and Aggregation 11. (Big) Data Visualization Part 4. Application: Topics in Big Data Econometrics 12. Bottlenecks in Everyday Data Analytics Tasks 13. Econometrics with GPUs 14. Regression Analysis and Categorization with Spark and R 15. Large-scale Text Analysis with sparklyr Part 5. Appendices Appendix A. GitHub Appendix B. R Basics Appendix C. Install Hadoop
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