Syllabus

Syllabus Policy (Addendum)

Computational Social Science as a field of inquiry is exhaustively broad, encompassing several non-mutually exclusive enterprises of social and political methodology. We do not have time to cover everything, nor would I feel adequate to instruct you on everything under the umbrella of CSS. Instead, this course principally focuses on

1. Extending your proficiency with programming languages (particularly R and, to a lesser extent, Python) -- especially the degree of “cool stuff” you are able to leverage these tools to explore.
2. Engaging both conceptually and practically in the application of various methodologies related to viewing text data as a valuable and worthwhile strategy for measuring political phenomena.

While we are generally limiting our scope to text analysis, this too is a bold undertaking. From bag-of-words to self-supervised machine learning and bidirectional encoders, there's a lot of material we can explore. Moreover, as you might imagine, how methodologically rigorous some of these can be understandably varies from topic to topic. My goal is to bake this reasonable learning curve into our progression -- hence why there are weeks where we continue the topic of discussion from those preceding. However, if I feel it necessary to slow our progression of topics in order to better reinforce comprehension of early or more introductory-level topics, I will do so and give you proper notice of such changes to the syllabus.