Python Programming for Multilingual Texts (DHSI 2026)

Event Language
EnglishFormat
in person/face-à-faceDescription
This course introduces digital history through multilingual text analysis using the Python coding language. It is designed for both beginners and those with some experience, teaching and revisiting Python basics through Jupyter Notebooks. Participants will complete a full digital history project, from sourcing primary materials to analysis and visualization. The course’s corpus will be Lady Montagu’s The Turkish Embassy Letters, detailing her travels to Constantinople (1716-18), which are significant for the history of medicine, and for examining travel and mobility, cultural exchanges between Europe and the Ottoman Empire. They also offer an ideal bilingual corpus for historical analysis as they were first published in English in 1763, then translated into French in 1764, and finally published in a parallel English-French edition in 1816. Participants learn to clean, analyze, and visualize multilingual data in Python while exploring how text technologies and analytical techniques differ across source languages, emphasizing the challenges and opportunities of working with non-English texts in the digital humanities. By the end of the course, participants will have the tools to design their own digital history project and will have gained hands-on experience in multilingual digital humanities. No prior experience with digital tools is required, though an interest in historical source analysis is encouraged.
Instructor(s)
Merve Tekgürler is a MS candidate in symbolic systems and a PhD candidate in history at Stanford University. Merve’s research spans multilingual digital humanities, artificial intelligence, and machine translation, focusing on the development of Natural Language Processing tools for Ottoman Turkish. They previously taught “Python Programming for Digital Humanities” at University of California, Berkeley.
Chloé Brault completed her PhD in comparative literature at Stanford in 2025. She works computationally with French-language texts from 1960s Quebec.
