Digital Humanities in Practice: Data Science for Text Research

Traditional humanities research often requires manually analyzing hundreds of texts, which can take years. By combining literary analysis with modern data science techniques, you can uncover patterns, trends, and connections across vast digital archives in a fraction of the time.\n\nThis text-based course guides you from fundamental humanities concepts to practical digital research. You will learn how to formulate computational research questions, prepare digital texts for analysis, and use basic programming concepts to extract meaningful insights from historical and literary documents.\n\nWhat you'll learn:\n- Understand the core principles of digital humanities and how to frame research questions computationally.\n- Prepare and clean unstructured text data, converting raw documents into structured formats like CSV and JSON.\n- Apply basic Python programming concepts specifically tailored for text mining and analysis.\n- Analyze word frequencies, patterns, and collocations across large collections of documents.\n- Explore modern text-analysis methodologies including basic natural language processing concepts.\n- Interpret your computational results to support qualitative literary or historical arguments.\n\nWe begin with the foundational concepts of digital humanities, ensuring you understand how to transition from traditional reading to digital analysis. You will then progress through step-by-step written explanations and practical exercises focusing on text manipulation, data cleanup, and basic analytical script reading.\n\nThis course is designed for absolute beginners in coding, including students, researchers, librarians, and historians who want to introduce digital methods into their work. No prior programming experience is required.\n\nStart your journey into digital research and unlock new perspectives hidden in your texts today.