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This is an unconventional methods course, because we’ll be looking at two very different kinds of methods in tandem. literature and code. Your work should be balanced between these, so you can think about how to use the methods we learn.

I assume that you have a computer that runs Windows, Mac OS X, or Ubuntu. If you use some more esoteric operating system, I will not help you with installation.

If you do not own a computer or if your computer is unsuitable for the work in this class (as a rule of thumb: if it cost less than $1,000 more than 4 years ago), we can set up an environment in the cloud for you to run RStudio in. I generally don’t advise this, though, because it makes it harder for you to continue your work later.

Readings and Attendance

You should attend class each week having completed the assigned readings and ready to discuss them. Let me know in advance if you are going to be missing class or if you must attend remotely.

Problem Sets

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 submitted as PDF, or docx files ‘knitted’ inside R markdown.

Sometimes it will be hard to get them to knit. Post for help on the Slack if you can’t get it to work.

They should be completed weekly, submitted over slack. Problem sets are required but ungraded;

Free projects

The course text will generally end with a ‘free project’ that presupposes that you are working with your own dataset. We’ll talk about these datasets starting in the second week; in the third week on, you can also use one that I’ll supply of historic New York City population.

Final Projects

These free projects are first stabs towards creating a work of analysis of your own. 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 conference-poster style exploration.

Keeping up to date

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:

  1. Any e-mails or announcements in class or on Slack.
  2. The current version of the syllabus on the course web site.
  3. The most recent paper copy of the syllabus handed out.

Absences and Coronavirus.

In order to protect people, you must not attend class while potentially carrying covid-19. It will generally be an option to Zoom into class if you alert me to a medical reason in advance.

Students attending remotely should generally have cameras on and engage with work in the class as though they were present.

Grading and effort.

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 individual areas that you read.

But it’s also important that you work consistently in this class for it to be of much use.

I have provided below a default rubric for success in this class. It adds up to 110% because I want you to have some choice for how to distribute your energies.

If you have goals that you feel will not be met here, please meet with me in the first two weeks of class and we can come up with an alternate grade contract.

  1. Submit the exercise portions of the problem sets each week demonstrating an engaged attempt to complete the problems. These should be done individually, but it’s fine to take from other people’s answers if you credit them. (20%)
  2. Post at least 6 of the free exercise attempts to the course slack for various weeks. These may be done collaboratively; they can be handed in up to a week after the rest of the associated problem set without repercussion. (20%)
  3. Bonus points for especially interesting free exercise attempts or deep engagement with readings in class. (10%)
  4. Complete various, short non-programming assignments described on syllabus, on schedule and usefully. (10%)
  5. Attend each class and participate actively and supportively in classroom discussions. (15%)
  6. Hand in a final project that demonstrates and develops your skills in the quantitative and descriptive parts of this class. (25%)