PhD level course Fall 22: Introduction to Cultural Analytics

The PhD course “Introduction to Cultural Analytics” is given by the Centre for Digital Humanities Uppsala (CDHU) in the fall term 2022. It starts in week 36, runs on part time (50%), and ends in week 47. The main instructors on the course will be Karl Berglund, Marie Dubremetz, and Ekta Vats. Formally responsible (kursansvarig) for the course are Anna Foka, and Isto Huvila, Department of ALM.

Registration for the course is now closed.

More information, including an outline of the course, can be found here. (Link removed)

The course is targeted towards PhD students in the humanities and social sciences who do not yet possess specific computational or technical skills, but who are interested to learn more for their future thesis work. Apart from some of the guest lectures, the course will be physical and take place at campus Engelska Parken, Uppsala University. The course is module based, which means that you are free to choose how many modules you would like to take. Each module equals 1.5 ECTS, or 2 weeks at 50 %. Modules 1–2 are mandatory, while modules 3–6 are optional. To make the course equal 7.5 ECTS you thus take module 1–2 plus an additional three modules. For details on each module, please see the attached plan.

Registration
If you are interested in taking the course, please check with your supervisors that it works and then send an email to karl.berglund@abm.uu.se, including your PhD topic, a short note of approval by your supervisors, and a sentence about why you are interested in the course. The course will be offered on a first come, first served basis. PhDs at Uppsala University will have priority, thereafter PhDs at other universities in Sweden. There will be a maximum of 10 participants. When the course is completed you will receive a certificate of participation.

Main course goals
The aim of the course is to introduce methods for computational text analysis from a humanities and social sciences perspective. This covers and pays equal attention to providing:

• knowledge about computational text analysis methods and their relevance for humanities and social sciences tasks
• knowledge about machine learning and its basic concepts, as well as such methods’ possibilities and limitations
• practical skills to employ computational methods for text analysis by using existing software, and by following and adapting basic programming scripts
• the ability to critically reflect upon the results derived from computational methods (regarding ethical, statistical, and empirical/material-oriented concerns)
• the ability to highlight epistemological concerns regarding computational and statistical methods from a humanities and social sciences perspective