The development of information technologies and the rise of the digital humanities have opened new, exciting avenues for historical research and for the engagement of historians with the public. History and the digital have intersected in ways that, first, reconfigure historical research through the extensive digitization of sources and the creation of computational tools to process historical data ("digital history"); second, offer a wealth of new objects for historical research ("historicizing the digital"). Accordingly, the course proposes not only to survey the main computational approaches and methods that can be used to study history, but also, drawing on a series of case studies from the history of science and technology, to critically reflect on what it means to think digitally. Students will develop small-group projects in digital history and will document their research in a final paper.

INTRODUCTION

Week 1. History, Science, History of Science

PART I: TEXTS

Week 2. Towards Big Data? Digitized and Born-Digital Sources in History

Week 3. History of Information Overload

Week 4. Text Analysis

PART II: NUMBERS

Week 5. Multiple Component Analysis & Regressions

Week 6. Trust in Numbers: Quantifying the World

Week 7. Network Analysis

PART III: IMAGES

Week 8. Data Visualization

Week 9. Image Analysis & Computer Vision

Week 10. Picturing Science: Drawings, Graphs, Diagrams

PART IV: THE PUBLIC

Week 11. Virtual Museums

Week 12. Science, the Public, and Invisible Technicians

CONCLUSION(S)

Week 13. A Critique of Digital Humanities

Week 14. Final Presentation of Projects

history, social sciences, digital, history of science, big data, text analysis, data visualization, citizen science, history of technology, digital humanities, computational thinking, ethics, science and society

None

nice to have:

- CS-401 (Applied data analysis) or equivalent

- a SHS course in history (for example: HUM-221, HUM-248, HUM-263 etc.)

• Identify and formulate important research questions in history
• Explore historical data using a variety of computational approaches
• Analyze the differences and similarities between the natural and the human/social sciences
• Contextualise her/his data science practice through historical examples

• Make an oral presentation.
• Write a scientific or technical report.
• Use a work methodology appropriate to the task.
• Demonstrate the capacity for critical thinking

Lectures + discussion of readings (2 hours per week)

Student projects (3 hours per week)

Students are expected to attend lectures, read the assigned articles, participate actively to class discussions, design and conduct projects in small groups, take the final in-class exam.

Project (70%): two intermediary reports (each 10%) and one final report (50%)

Final written exam, in class (30%)

Lisa Gitelman (ed.), "Raw Data" is an Oxymoron, Cambridge, Mass.: MIT Press, 2013.

Shawn Graham, Ian Milligan and Scott Weingart, Exploring Big Historical Data, The Historian's Macroscope, London: Imperial College Press, 2015.

Jo Guldi and David Armitage, The History Manifesto, Cambridge: Cambridge University Press, 2014.

Ian Milligan, "Mining the 'Internet Graveyard': Rethinking the Historian's Toolkit," Journal of the Canadian Historical Association, 23(2), 2015: 21-64.

• Ian Milligan, "Mining the 'Internet Graveyard': Rethinking the Historian's Toolkit,"https://www.erudit.org/fr/revues/jcha/2012-v23-n2-jcha0589/1015788ar.pdf
• Find the references at the Libraryhttps://slsp-epfl.primo.exlibrisgroup.com/discovery/search?query=course_code,contains,%22DH-412%202025-2026%22&tab=41SLSP_EPF_MyInst_and_CI&search_scope=MyInst_and_CI&vid=41SLSP_EPF:prod

• https://go.epfl.ch/DH-412https://go.epfl.ch/DH-412