This course covers concepts and applications of social networks and big data. The fundamentals of social networks and big data are introduced, and case studies of related issues are provided. Literature of recent developments on theoretical, algorithmic and application aspects of big data in social networks will be read. Approaches for big data analytics, such as data processing and visualization and their relations with complex network analysis are studied. Also, R language is used for data analysis and visualization.This course covers concepts and applications of social networks and big data. The fundamentals of social networks and big data are introduced, and case studies of related issues are provided. Literature of recent developments on theoretical, algorithmic and application aspects of big data in social networks will be read. Approaches for big data analytics, such as data processing and visualization and their relationships with complex network analysis are studied. Also, R language is used for data analysis and visualization.
1. Big Data Fundamentals Concepts, Drivers & Techniques
by Thomas Erl,Wajid Khattak,and Paul Buhler
Prentice Hall
2. R Programming for Data Science
by Roger D. Peng
1. Big Data Fundamentals Concepts, Drivers & Techniques
by Thomas Erl, Wajid Khattak, and Paul Buhler
Prentice Hall
2. R Programming for Data Science
by Roger D. Peng
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| 平常成績平常成績 Normal achievements |
40 | |
| 報告報告 report |
60 |
