Brief Course Description
1. Course Title:
Social Network Data Analytics
2. Language of Instruction:
Tiếng Việt
3. Course Code:
M01188
4. Credits:
3
5. Course Objectives:
This course focuses on mining data from social networks such as Twitter, Facebook, LinkedIn, Google+, and GitHub. Students will learn how to use open-source tools to retrieve data, process data, and visualize the collected data.
6. Brief Description of Course Content:
This course focuses on harnessing data from social networks such as Twitter, Facebook, LinkedIn, Google+, and GitHub. Students will learn how to use open source tools to retrieve data, process data, and visualize collected data.
The course content consists of five parts
•Chapter 1: Overview of data mining from social networks: In this chapter, students will be introduced to the basic concepts of data mining from social networks, including the applications and challenges of the field.
Chapter 2: Using APIs to Get Data from Twitter, Facebook, and LinkedIn: This chapter focuses on how to use Twitter, Facebook, and LinkedIn APIs to get data. Students will learn how to make API requests, handle returned data formats, and create computer programs to retrieve data.
•Chapter 3: Processing and Analyzing Collected Data: In this chapter, students will learn how to use Python tools to process data collected from social networks. These tools include the pandas library for processing data in the form of tables and the matplotlib library for data visualization.
•Chapter 4: Data visualization with charts and graphs: In this chapter, students will learn how to use Python tools to visualize data collected from social networks using charts and graphs. These tools include the seaborn library for drawing statistical graphs and the network library for plotting link graphs.
•Chapter 5: Application of Data Mining Techniques: This chapter focuses on data mining techniques to find information and detect connections from data collected from social networks. Students will learn how to use these techniques to analyze data and find specific information, for example searching for popular hashtags on Twitter or searching for similar user groups on LinkedIn.
All chapters in the course use the Python programming language and popular libraries such as pandas, matplotlib, seaborn, network, and other software development tools.