This is an unconventional methods course, because we’ll be looking at two very different kinds of methods; literature and code.
You must attend class each week having completed the assigned readings and ready to discuss them. This may be a tiny class; let me know in advance if you are going to be missing.
To help consolidate your programming abilities, there will be weekly problem sets. The first can be completed in any form you like; later ones should be mailed to me as R Markdown Documents. (This is the format I’ll be sending them to you as.)
They should be completed weekly, and e-mailed to me before the start of class. Problem sets are required but ungraded–I understand that you may not be able to complete them every week. The purpose is to try.
Once we get our feet wet, I’ll ask you to post results of data explorations online. If I were you, I’d do this on a blog or straight to social media. Also keep them coming to class. This can be on one of the large unstructured sets I provide, or another data source you work out with me in class.
Later we’ll be exploring a series of specific algorithms. Some we’ll go over in depth in class; others we’ll only touch on obliquely. Based on the data from class you find most interesting (or data of your own, we’ll work to determine which of those algorithms may make sense as a transformation. You’ll then write up a short version of the exploration for the course blog, present it briefly in class, and then revise your post in response to comments.
If you are taking this under the guise of a research seminar with your own materials, you should produce a multifaceted analysis with a reflective, methodological take on the data you bring to the class. This could either take the form of an explicit journal article for a digital humanities audience (I would mirror those in Digital Scholarship in the Humanities or Cultural Analytics) or a 10-20 methodological appendix to a larger work (such as a dissertation) giving the details of an analysis that may take only a few pages in a more traditional work. You’ll consult with me over the semester about how best to integrate your sources with materials from class.
If you are taking it as a readings course, you still should create something. As we move into the later weeks of the semester, you should figure out which of the the various data sets we’ve used may be particularly interesting and find a way to build out on the techniques and strategies to create something novel. Most likely, this will be an experiment along the lines of the “Quantitative Formalism” pamphlet we read later. In it, you will take the fundamental advantages of a programming environment combine some of the various methods and strategies we’ve learned in a programming environment, or build out some new ones. Appropriate products might include a large-format print map, a set of blog posts exploring generic distances, or a poster proposal.
This is an advanced graduate seminar; I hope that the syllabus will change in response to your own interests and readings.
This flexibility may cause problems of “versioning”: what version of the syllabus should you believe? So for the record, the priority for what to do consists of:
Grading in this course is thorny. As graduate students, you should be starting to get a sense of what is important to you; I’m not going to quiz you on the parts of books that you find interesting.
Grading is based on how fully you do the following: