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79698-01 - Lecture with practical courses: Introduction to Digital Studies: Quantitative and Computational Approaches (4 CP)
| Semester | fall semester 2026 |
| Course frequency | Irregular |
| Lecturers | Rosa Blakeman-Lavelle (rosa.lavelle-hill@unibas.ch, Assessor) |
| Content | ! Important: This course has evolved from the former course “Introduction to Digital Humanities” which ran in Fall 2025 (and earlier). Students who have taken that course are therefore not permitted to take this course for additional credits. This course provides an introduction to quantitative and computational approaches to digital studies in the humanities and social sciences. It introduces students to a broad range of methods for working with digital data and demonstrates how computational approaches can be used to investigate research questions across different humanities and social science topics. The course covers key concepts and methods relating to Big Data, artificial intelligence (AI), natural language processing (NLP), large language models (LLMs), optical character recognition (OCR), computer vision, digital imaging, metadata, databases, network analysis, linked open data, corpus linguistics, digital book and writing analysis, web scraping, and application programming interfaces (APIs). Students are also introduced to AI-assisted research practices, including AI prompting and image upscaling, as well as tools for scientific writing and literature review. Alongside methodological skills, the course encourages students to critically reflect on the opportunities and limitations of computational approaches. Topics such as data quality, copyright, responsible use of AI, and the interpretation and communication of computational research are integrated throughout the course. The course follows a flipped-classroom format. Students engage independently with online lectures and assigned readings before attending physical sessions. Class time is then used to discuss and consolidate this material, clarify key concepts, connect different methodological approaches, and apply them in practical exercises. Short quizzes at the beginning of selected sessions assess students’ understanding of the preparatory material. Practical exercise sessions allow students to gain experience applying the approaches introduced in the course. The final part of the course supports students in developing an independently researched scientific essay. |
| Learning objectives | Upon successful completion of the course, students will be able to: • understand the basic principles and terminology of quantitative and computational approaches; • describe the foundations and applications of NLP, OCR, LLMs, computer vision, databases, network analysis, linked open data, corpus linguistics, web scraping, and APIs; • critically assess the opportunities and limitations of AI and computational methods for humanities and interdisciplinary research; • use appropriate tools and strategies for scientific literature searching, referencing and academic writing; • independently identify and engage with relevant scientific literature and develop a structured scientific argument in written form. The course uses a flipped-classroom approach combining asynchronous self-study with physical lecture and exercise sessions. Before each thematic block, students watch assigned online lectures and complete the required readings. Physical sessions combine short quizzes, discussion of the preparatory material, supplementary teaching, demonstrations, and guided practical exercises. The course consists of thematic blocks on: (1) Big Data, AI, NLP, OCR and LLMs; (2) metadata, computer vision, digital imaging and AI upscaling; (3) databases, network analysis, linked open data and corpus linguistics; (4) digital book and writing analysis, web scraping and APIs; and (5) scientific writing, literature review and copyright. A final optional drop-in session provides support for the written assignment. |
| Bibliography | Natural Language Processing Required and recommended literature will be provided via ADAM for the relevant thematic blocks. |
| Comments | Students are expected to complete the assigned online lectures and readings before attending the corresponding physical session. The preparatory material forms the basis of the short quizzes, classroom discussions, and practical exercises. The course is interdisciplinary and is intended as an introduction to computational and quantitative approaches. Previous advanced programming experience is not required. Students should, however, be prepared to engage actively with digital tools and to complete independent practical work between sessions. |
| Language of instruction | English |
| Use of digital media | No specific media used |
| Interval | Weekday | Time | Room |
|---|---|---|---|
| wöchentlich | Thursday | 10.15-12.00 | Rosshofgasse (Schnitz), Seminarraum S 01 |
| wöchentlich | Thursday | 13.15-15.00 | Kollegienhaus, Hörsaal 115 |
Dates
| Date | Time | Room |
|---|---|---|
| Thursday 24.09.2026 | 10.15-12.00 | Rosshofgasse (Schnitz), Seminarraum S 01 |
| Thursday 24.09.2026 | 13.15-15.00 | Rosshofgasse (Schnitz), Seminarraum S 02 |
| Thursday 01.10.2026 | 10.15-12.00 | Rosshofgasse (Schnitz), Seminarraum S 01 |
| Thursday 01.10.2026 | 13.15-15.00 | Kollegienhaus, Hörsaal 115 |
| Thursday 08.10.2026 | 10.15-12.00 | Rosshofgasse (Schnitz), Seminarraum S 01 |
| Thursday 08.10.2026 | 13.15-15.00 | Kollegienhaus, Hörsaal 115 |
| Thursday 15.10.2026 | 10.15-12.00 | Rosshofgasse (Schnitz), Seminarraum S 01 |
| Thursday 15.10.2026 | 13.15-15.00 | Kollegienhaus, Hörsaal 115 |
| Thursday 22.10.2026 | 10.15-12.00 | Rosshofgasse (Schnitz), Seminarraum S 01 |
| Thursday 22.10.2026 | 13.15-15.00 | Kollegienhaus, Hörsaal 115 |
| Thursday 29.10.2026 | 10.15-12.00 | Rosshofgasse (Schnitz), Seminarraum S 01 |
| Thursday 29.10.2026 | 13.15-15.00 | Kollegienhaus, Hörsaal 115 |
| Thursday 05.11.2026 | 10.15-12.00 | Rosshofgasse (Schnitz), Seminarraum S 01 |
| Thursday 05.11.2026 | 13.15-15.00 | Kollegienhaus, Hörsaal 115 |
| Thursday 12.11.2026 | 10.15-12.00 | Rosshofgasse (Schnitz), Seminarraum S 01 |
| Thursday 12.11.2026 | 13.15-15.00 | Kollegienhaus, Hörsaal 115 |
| Thursday 19.11.2026 | 10.15-12.00 | Rosshofgasse (Schnitz), Seminarraum S 01 |
| Thursday 19.11.2026 | 13.15-15.00 | Kollegienhaus, Hörsaal 115 |
| Thursday 26.11.2026 | 10.15-12.00 | Rosshofgasse (Schnitz), Seminarraum S 01 |
| Thursday 26.11.2026 | 13.15-15.00 | Kollegienhaus, Hörsaal 115 |
| Thursday 03.12.2026 | 10.15-12.00 | Rosshofgasse (Schnitz), Seminarraum S 01 |
| Thursday 03.12.2026 | 13.15-15.00 | Kollegienhaus, Hörsaal 115 |
| Thursday 10.12.2026 | 10.15-12.00 | Rosshofgasse (Schnitz), Seminarraum S 01 |
| Thursday 10.12.2026 | 13.15-15.00 | Kollegienhaus, Hörsaal 115 |
| Thursday 17.12.2026 | 10.15-12.00 | Rosshofgasse (Schnitz), Seminarraum S 01 |
| Thursday 17.12.2026 | 13.15-15.00 | Kollegienhaus, Hörsaal 115 |
| Modules |
Modul: Digital Humanities, Culture and Society (Master's degree subject: Digital Humanities) Module: Introduction to Digital Humanities (Master's degree subject: Digital Humanities) Module: Societal Approaches (Master's Studies: European Global Studies) |
| Assessment format | continuous assessment |
| Assessment details | Assessment is pass/fail and consists of class quizzes, practical homework exercises, participation in physical sessions, and a final written assignment. AI tools, including large language models, may be used to support learning and the development of students’ own work, but they may not be used to fully solve the exercises or replace students’ independent academic work. Any use of AI tools must be transparently disclosed and cited according to the University of Basel guidelines. Failure to clearly disclose the use of AI may result in an exercise or assignment being assessed as failed. |
| Assessment registration/deregistration | Reg.: course registration; dereg.: not required |
| Repeat examination | no repeat examination |
| Scale | Pass / Fail |
| Repeated registration | as often as necessary |
| Responsible faculty | Faculty of Humanities and Social Sciences, studadmin-philhist@unibas.ch |
| Offered by | Digital Humanities Lab |