Data Analysis and Programming for Social Sciences and Humanities

Research in the humanities and social sciences increasingly relies on digital data, yet learning to code can feel intimidating when you come from a non-technical background. This text-based course bridges that gap, introducing programming and data literacy through a gentle, research-focused lens. You will transition from manual, time-consuming research methods to programmatic data collection, cleaning, and analysis, enabling you to uncover deep patterns in texts, surveys, and historical records. What you'll learn: - Understand core data concepts, file structures, and basic programming logic tailored for qualitative researchers. - Read and clean unstructured textual data, historical archives, and survey results using modern Python libraries. - Apply basic statistical methods to social science datasets to validate your research hypotheses. - Practice structured data collection and basic web scraping techniques to gather research materials programmatically. - Explore modern data workflows, including virtual environments and type hints, to keep your research code organized and reproducible. Starting with fundamental definitions of data types and essential programming concepts, this course guides you step-by-step through practical text processing and data analysis techniques using written explanations and structured code exercises. This course is designed specifically for students, researchers, and professionals in the humanities and social sciences with absolutely zero prior programming experience. Start your journey into digital humanities and data-driven social research today.