Methodologies and Materials

Methodologies:

The class will be taught in a very hands on way. It will also involve discussion of selected pages of short readings and writing some reflective essays about them. Over the semester we will try out different digital environments–from the easy to the less easy–and use prepared materials to explore analysis of text with computers. We will not be “coding” ourselves from scratch, but rather experimenting with the methods of others to see what kinds of results we can achieve.

It will be important to bring your laptop everyday. There will be lab-like portion of the course in which we will practice straightforward assignments using a programming language known as “R.” There will be four short 2 page writing assignments that will draw on our in-class exercises and a final project of your own design. Emphasis will be placed, less on “getting it right” than on experimentation. The final project will involve students building a “model” and analyzing a body of texts using different techniques and reflecting on the insights they provided.

The course is structured to deal with works written in English, although students may explore texts written in other languages. The course will be interesting to a number of majors who work with texts (history, literature/creative writing, museum studies, social sciences, political studies). The texts that we will use will come from both history and literature, but the course will introduce a number of skills that are highly transferable to many fields of research and human inquiry and encourage students to reflect critically on those skills.

Materials (none of these are required purchases):

Rockwell/Sinclair, Hermeneutica: Computer-Assisted Interpretation in the Humanities (MIT Press, 2016) [Available as e-book from NYUAD eBook central]

Silge/Robinson, Text Mining with R: A Tidy Approach (O’Reilly, 2017) Online updated version here. 

DataCamp, Introduction to the Tidyverse (videos)

Selected lessons from the Programming Historian

Other reading selections linked to the course site or in Drive.

You may find that after this class you want to “read like a computer” in other disciplines.  Here are some suggestions for learning more:

Finance: Analyzing Financial and Economic Data with R
Journalism: R for Journalists
Humanities: Humanities Data in R
Biology: Data Carpentry for Genomics | Bioinformatics
Social Science: Using R for Data Analysis in Social Science | Data Carpentry for Social Sciences
Business/Data Science: R for Data Science
History: Computational Historical Thinking
Business: Business Analytics R Programming