Foundations of Computational Social Science

Ever wonder how large-scale digital data from the web and social media can reveal insights into human society? This course introduces you to the exciting field of computational social science, where you'll learn to use programming to explore complex social questions in a systematic way. Upon completion, you'll have a solid framework for analyzing social phenomena through a computational lens. You will be prepared to collect data from digital sources, apply analytical techniques like network analysis and text mining, and interpret your findings to understand complex social patterns, from online discussions to large-scale societal trends. What you'll learn: - Learn the core principles and ethical considerations of computational social science. - Practice collecting data from the web using APIs and basic scraping techniques with Python. - Analyze textual data to uncover sentiment and topics using modern natural language processing methods. - Build and interpret network graphs to understand relationships and influence in social systems. - Apply fundamental machine learning concepts to classify and predict social outcomes. - Understand the principles of agent-based modeling to simulate social dynamics. - Evaluate datasets and models for potential bias to ensure a responsible and fair analysis. The course begins with the fundamental concepts and ethical frameworks of the field. You'll then progress through hands-on topics, learning to apply computational methods to social data through practical written exercises. This course is designed for absolute beginners. No prior experience in programming or social science research is required, just a curiosity about how data can help us understand the world. Start your journey into data-driven social inquiry today.