Computational Social Science: Practical Data Project

How do online communities form, and what can digital footprints tell us about human behavior? Computational social science bridges the gap between social inquiry and data science, allowing us to study social interactions at scale. In this practical project-based course, you will walk through the entire lifecycle of a computational social science study. You will transition from understanding core theoretical frameworks to executing a complete research project: ethically gathering social data, processing text, analyzing networks, and interpreting findings. By reading structured explanations and studying real-world code examples, you will gain the confidence to design and execute your own digital social research. What you'll learn: - Understand the core concepts, ethics, and methodologies of computational social science - Gather digital trace data ethically from social platforms and APIs using Python - Clean and structure unstructured social media text for systematic analysis - Apply modern natural language processing techniques to analyze sentiment and themes - Analyze social networks to discover influential nodes and community structures - Interpret data patterns to draw meaningful sociological and behavioral conclusions You will start by exploring the foundational ethics of digital research before moving step-by-step through data collection, text mining, and network analysis. Each concept is reinforced with clear written explanations and practical code walkthroughs. This course is designed for beginners in data analytics, sociology, or digital humanities who want to apply computational methods to social questions. No advanced programming or statistical background is required. Start your journey into computational social science and unlock insights from digital human behavior today.