Computational Social Science Methods

This module, Computational Social Science (CSS), provides a foundational understanding of the digital revolution and the challenges faced by social science in the current era. Learners will explore the history of science, social emergence, and the limitations of induction and deduction. The module also introduces the concept of computational social science and its learning goals, providing a strong foundation for subsequent modules.

The module, Example of Computational Social Science: Data Science, focuses on the practical application of computational social science through data analysis. Learners will delve into big data analysis, particularly its role in poverty analysis, and gain hands-on experience in web scraping techniques. The module prepares learners to apply computational methods to real-world societal challenges.

This module, Examples of CSS: Machine Learning & AI, delves into the intersection of artificial intelligence, machine learning, and computational social science. Learners will explore the concepts of overfitting, training, validation, and testing in machine learning, and gain insights into the application of artificial intelligence in music. The module equips learners with the knowledge to leverage machine learning and AI in social science research.

The module, Examples of CSS: Social Networks and Computer Simulations, investigates the impact of social networks and computer simulations on societal dynamics. Learners will explore the interconnected nature of social networks, their influence on human behavior, and the use of computer simulations to model cultural boundaries and social emergence. This module provides a holistic understanding of the role of technology in shaping social systems and patterns.