Examination arrangement

Course content

The onset of big data has revolutionised how governments and the business sector make decisions. Likewise, the increasing availability of historical quantitative information provides an excellent opportunity to fully exploit the analysis of large datasets to expand our knowledge of the past. This course provides an in-depth introduction to quantitative methods, covering some of the techniques most widely used in research in the historical and social sciences. Combining lectures and computer practicals, this hands-on course shows how to apply quantitative methods to historical information, both textual and numerical, keeping statistical theory and mathematics to a minimum. The goal is to provide students with the tools to critically engage with the literature relying on quantitative methods and to be able to conduct original research using those tools either in academia, the public or the business sector. In the process, students will master R, a statistical software widely used by practitioners in many different fields. Please note that if students plan to include this course in their coursework component, it must be preapproved by the relevant head of the study/PhD programme in cooperation with their supervisor.

Learning outcome

A candidate who satisfactorily passes the course will be able to: - critically engage with studies relying on quantitative methods, - conduct original research using those tools either in academia, the public or the business sector, - master R, a statistical software widely used by practitioners in many different fields.

Learning methods and activities

30 hours: 10 three-hour sessions (one-week intencive course). Each session combines lecturing (aprox. 1 hour) with computer practicals (aprox. 2 hours). Every session combines lectures and computer practicals using R. This is an eminently hands-on course which keeps statistical theory and mathematics to a minimum. Students will learn by applying the different concepts to real data used by historians (both textual and numerical). Further information will be provided at the beginning of the course.

Compulsory assignments

• Participation
• 10 approved exercises

Further on evaluation

Compulsory attendance and compulsory session assignments (10 in total). Additionally, PhD students will require passing a take-home exam at the end of the course. A set of readings will also be assigned in preparation for the course. Total workload of the course: Preliminary readings + 30 hours teaching + 10 assignments + Take home exam

Recommended previous knowledge

No previous statistical background is required.

Required previous knowledge

BA in History or equivalent. Admission to a relevant study programme is required.

Course materials

Course materials and assignments will be provided during the course.

Credit reductions