Examination arrangement

Course content

Historians have digital access to records on an unprecedent scale. Millions of newspapers, government documents, letters and diaries, among other sources, are only one click away and completely searchable. Similarly, complete population censuses, birth, death and marriage records, military and prisons registers, and other sources have been digitalised and made available online.

How can we make sense of this ever-increasing wealth of information? Computational methods permit extracting and analysing huge amounts of information, both textual and numerical, that would be impossible otherwise. Supplementing traditional qualitative methods with computing methods not only allows shedding new light into old questions, but also addressing new ones. Likewise, computational tools help visualising information in innovative and powerful ways, thus producing compelling arguments and stories.

This course provides an in-depth introduction to computational methods in history by applying these tools to real historical information, both quantitative and qualitative. It will also equip students with the necessary background to understand and interpret the historical literature using these methods. The goal is to provide students with the tools to critically engage with the literature relying on computing methods and to be able to conduct original research using these tools in academia, the public or the private sector.

No previous background in computing or statistics is required.

The course revolves around three main themes:

1. Statistics and Big Data: Descriptive statistics, correlation and regression analysis.
2. Computational Text Analysis: Digital corpus management, word frequency & dictionary methods, text classification, topic models and sentiment analysis.
3. Geographic Information Systems (GIS): Visualising spatial information and digitalising historical maps.

Learning outcome

A candidate who passes this course is expected to have the following learning outcome according to the course curriculum, defined as knowledge and skills:

Knowledge:

• Critically engage with studies relying on computing tools.
• Gain an introductory technical knowledge on many computational methods.

Skills:

• Formulate research questions of their own that can be answered using these tools.
• Conduct original research using these methods in academia, the public, or the business sector.
• Continue developing these skills based on the foundations provided in this course.

Learning methods and activities

The course is structured into 8 three-hour sessions combining lectures and applied sessions behind a computer.

Further on evaluation

Apart from actively participating in the sessions, students are expected to deliver a take-home assignment after the course is finished.

Recommended previous knowledge

No previous background in computing or statistics is required.

Required previous knowledge

none

Course materials

See curriculum published at the start of the semester, and other materials (reading and lecture lists and other relevant information) published on Blackboard