Foundations of Computational Social Science Methods

Digital interactions generate massive amounts of data that reveal how we think, move, and connect. Understanding how to analyze this data through a social science lens is essential for navigating today's data-driven world. This course provides a comprehensive introduction to the field, moving from core definitions to the practical application of computational tools to understand human behavior. You will learn to bridge the gap between traditional social research and modern data science. By the end of the course, you will be able to identify patterns in digital footprints and understand the mechanisms that drive social trends. What you'll learn: - Understand the fundamental principles of computational social science and its impact on modern society. - Explore methods for collecting digital footprint data from various online platforms and services. - Analyze social networks and communication patterns using quantitative modeling techniques. - Apply automated text analysis to uncover trends in public discourse and sentiment. - Evaluate the ethical implications of data collection, algorithmic bias, and privacy in social research. - Practice interpreting complex social datasets to draw meaningful and actionable conclusions. The curriculum begins with foundational concepts and terminology before progressing through data collection strategies and analysis methods for social networks and large-scale text data. You will read through clear explanations of how algorithms shape social interactions and how to study these effects systematically. This course is designed for beginners with an interest in social research and data; no prior programming or advanced statistical experience is required. Start your journey into the data-driven study of human society today.