BA History and Russian

Contemporary debates around big data are often infused with a sense of novelty. While much is indeed new about how corporations, governments, and scholars use information today, the ways in which they create, order, visualise, and analyse data emerged over centuries. This course places contemporary enthusiasm and apprehension around big data and the technologies devised to make it legible – from diagrams to artificial intelligence – in a long historical perspective. Approaching data as a historical category and as a source, we will find that our present age is hardly the first in which human societies have agonized over the problem of information overload. By examining past and present forms and uses of data – including knots, punch cards, and language models – we will develop a deeper understanding of how data, and its associated practices, have been established and confronted in different historical and geographic settings.

The unit aims to: 

• Place contemporary enthusiasm and apprehension around big data and datafication in historical perspective
• Equip students with strong conceptual and methodological foundations for interacting with data and thinking about its history and biases 
• Support students in examining the relationships between current and historic data practices in a variety of settings

Upon completion of this course, students will be expected to have developed a range of transferable skills. Lectures, readings, and seminar discussions will allow students to develop their critical thinking skills and digital literacy and apply those to a wide range of examples. The first assignment will enable students to navigate a complex historical record, identify relevant material, and offer a succinct and precise discussion. The second assignment will enable students to conceive and pursue a small independent research project and enhance their work through peer feedback. Throughout, students will develop a situated, in-depth understanding of data, datafication, and their associated practices.

The course will open in our present historical moment of heightened awareness of big data and move backwards in time to trace the histories of data. Lectures and seminars are structured chronologically and thematically and will cover key questions and turning points in the history of data and digital technology. Themes covered in the course include such topics as shifting understandings and uses of personal data, the role of data for the study of climate change, the evolution of predictive analytics and machine learning, the history of information visualisation, and constructions and erasures of gender and sexuality in census data.

The unit consists of 1h-long lectures and 2h-long seminars a week, delivered in person. 

Lectures will combine oral presentations with presentation slides, audio and video material, as well as interactive elements. 

Seminars will give students an opportunity to critically discuss the readings and lectures and involve them in a series of practical and group activities, including but not limited to debates, tutorials, and peer review.

• Place contemporary debates and practices of data and datafication in a historical perspective. 
• Identity the relationships between current and historic data practices in a variety of contexts. 
• Demonstrate a robust academic vocabulary for interacting with and questioning data.

• Situate and evaluate historical factors that have determined how data has been collected, organised, and made accessible or unavailable. 
• Examine and critique academic claims and arguments relating to the histories of data and datafication. 
• Identify, describe, and critically evaluate artifacts such as datasets, data visualisations, and related media.

• Identify, retrieve, and analyse different types of historical data and secondary readings. 
• Compare and contrast a range of methods and tools used to interrogate and analyse data in historical inquiry. 
• Analyse and recast a complex type of evidence in terms that peers and non-experts can understand.

• Think creatively about how to develop and communicate their work. 
• Reflect on and act upon peer and instructor feedback. 
• Summarise and present information and arguments with due regard to the target audience.

Ascher, Marcia. “The Logical-Numerical System of Inca Quipus.” Annals of the History of Computing 5, no. 3 (1983): 268–78.

Bouk, Dan. “The History and Political Economy of Personal Data over the Last Two Centuries in Three Acts.” Osiris 32, no. 1 (2017): 85–106.

Edwards, Paul N. A Vast Machine: Computer Models, Climate Data, and the Politics of Global Warming. Cambridge, MA: MIT Press, 2010.

Halpern, Orit. Beautiful Data: A History of Vision and Reason since 1945. Durham, NC: Duke University Press, 2015.

Igo, Sarah E. “Me and My Data.” Historical Studies in the Natural Sciences 48, no. 5 (2018): 616–26.

Jones, Matthew L. “How We Became Instrumentalists (Again): Data Positivism since World War II.” Historical Studies in the Natural Sciences 48, no. 5 (2018): 673–84.