Digital Humanities in Practice: Text Mining and Data Analysis

How can we analyze thousands of historical documents or literary works without spending years reading them manually? Digital humanities bridges the gap between traditional research and modern data science, allowing you to uncover hidden patterns, trends, and connections across massive collections of text. This course introduces you to the essential concepts and tools needed to convert qualitative research questions into structured data and meaningful insights. By reading through clear explanations and analyzing practical code examples, you will learn how to approach humanities data with a computational mindset. You will start with the absolute basics of text processing before moving on to structured analysis, ensuring you build a solid foundation even if you have never written a line of code before. What you'll learn: - Understand the foundational concepts of digital humanities and computational text analysis - Clean and prepare raw text data from digital archives for structured analysis - Apply basic Python text mining techniques to count word frequencies and analyze vocabulary - Perform sentiment analysis and basic topic modeling to discover themes in large document collections - Structure digital research projects from an initial hypothesis to final data-driven results - Navigate ethical considerations, bias, and limitations in digital archives and algorithms This text-based course guides you step-by-step from foundational terminology to practical research workflows, using clear examples and conceptual breakdowns. You will explore how to format data, run basic analyses, and interpret your findings scientifically. This course is designed specifically for students, researchers, librarians, and historians who are completely new to coding and want to add digital methods to their research toolkit. No prior programming or data science experience is required. Start exploring your texts in a whole new way and unlock computational insights today.