Digital Humanities in Practice: Text Mining and Data Analysis for Research

Traditional reading methods limit how much text a researcher can analyze at once. By introducing basic data science techniques to the humanities, you can analyze thousands of digital documents simultaneously, saving time and uncovering patterns that are invisible to the naked eye. This text-only course guides you from foundational concepts to practical digital research workflows. You will start by learning key terminology, data formats, and the core methodologies that connect computational tools with literary analysis. Through step-by-step written explanations and code examples, you will learn how to prepare textual data, perform basic text mining, and interpret your results within a humanities framework. What you'll learn: - Understand the foundational concepts of digital humanities and computational text analysis - Clean and prepare raw digital texts and unstructured data for analysis - Apply basic Python text mining techniques to find patterns across large document collections - Perform sentiment analysis and word frequency tracking on historical or literary texts - Structure and document your digital research workflow to ensure reproducible results This course begins with basic definitions and simple text manipulation before moving into structured data analysis and text mining techniques. It is designed specifically for beginners in literature, history, and the broader humanities with no prior coding or data science experience. Start exploring your texts in a whole new way today.