IS434

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Social Analytics and Applications

Course (UG/PG)

Undergraduate

Offering Unit/Department

Course Description

In today’s globally connected, online and mobile world, social media platforms are fast becoming the dominant means of communication and it is revolutionizing the way businesses communicate with their customers. Many popular social media platforms such as Facebook and Twitter allow for instant, real-time multi-way communication. Collecting and analysing data from multiple online sources require an Information Technology infrastructure. The data collected from online sources create a gold mine for businesses that want to understand and predict consumer and market behaviour. By leveraging sophisticated computing technologies, big data analytics can produce actionable insights valuable to the core operations of the business.

This course will explore emerging methods and applications for understanding online user behaviour on popular social media platforms. Students will be exposed to a variety of real-world business cases, a collection of data analytics tools, best practices and hands-on exercises. Students will learn how to 1) identify analytics problems, 2) use data analytics tools and identify types of analysis to be performed, and 3) close the loop (the process of taking the analysis results and interpreting it contextually).

Course Learning Outcomes

1. Demonstrate understanding of the business value of social analytics and how technologies can be used to create this value.

2. Develop web crawling programs to pull web-based data from remote servers.

3. Develop web scraping programs to extract relevant textual data.

4. Summarise unstructured textual data to understand social sentiment and trending topics.

5. Analyse and visualise social sentiment and trending topics.

6. Develop crawling programs to pull social media data from social media platforms via API (Application Programming Interface).

7. Analyse social relationships amongst different entities.

8. Analyse and visualise social influencers.

9. Compare and assess storage platforms for social data.

10. Build social analytics pipelines using cloud computing services.

Discipline-Specific Competencies

Data Analytics, Computational Modelling, Data Engineering, Pattern Recognition Systems, Text Analytics and Processing

SMU Graduate Learning Outcomes

Disciplinary Knowledge, Critical thinking & problem solving, Innovation and enterprising skills, Collaboration and leadership, Communication, Self-directed learning

Grading Basis

GRD - Graded