Syllabi for spring 2017

This semester I am teaching three courses, two undergraduate and one graduate. “Global History of Christianity” is a new course which Mack Holt, John Turner, and I are team teaching in both history and religious studies. This course was Mack’s idea, and the team teaching this semester is a one-time deal, but I am very glad that it’s been approved for the catalog going forward. “The Digital Past” is taught by a number of people at Mason, but I’ve decided to organize mine around the theme of “Reconstruction and Redemption.” For the graduate students, I’ve taught a version of “Data and Visualization in Digital History” before, but this is the first time that I’m teaching it as one of the required courses in the PhD sequence instead of as an elective.

Here are the syllabi and course descriptions.

Global History of Christianity, syllabus

This course is organized around a comparative examination of the many forms of global Christianity over the past two thousand years. Chronologically, it begins with the ancient Jewish, Greek, and Roman contexts of early Christianity and continues through the present. Students will become familiar with many kinds of Christianity across the globe, including Asian, African, Latin American, European, and North American Christianities. In each geographic and chronological contexts, students will explore several themes: use of sacred texts and the experiences of a typical church service, the relationship between Christianity and politics, and cultural aspects such as marriage and sexuality. Students will also consider Christianity as a series of global systems organized around missions, migration, trade, and warfare.

The Digital Past: Reconstruction and Redemption, syllabus

In this class, you will to learn to do history using digital tools. The course—which satisfies the Mason Core IT requirement—teaches the fundamentals of information technology by applying them to practical problems in history. Throughout the semester, you will work individually and with classmates on a series of projects about American history during the period of Reconstruction. You will learn how to do research online, but also how to put those sources in the context of other scholarly work. You will gather data, learn how to question it, analyze it, summarize it, and interpret it. You will create visualizations of datasets, including maps. You will learn how to present visual and textual sources online in web exhibits, and you will learn how to write and publish effectively online. Through learning by doing, you will gain both digital skills and the skills of a historian. This combination will be useful to you throughout your university career and in your future work.

Data and Visualization in Digital History, syllabus

In this methods course you will be introduced to data analysis and visualization for historians. You will learn to work with historical data, including finding, gathering, manipulating, analyzing, visualizing, and arguing from data, with special attention to geospatial, textual, and network data. These methods will be taught primarily through scripting in the R programming language, using other command line tools as appropriate. You will also learn how to present history on the web with HTML and CSS. While historical methods can be applied to many topics and time periods, they cannot be understood separate from how the discipline forms meaningful questions and interpretations, nor divorced from the particularities of the sources and histories of some specific topic. Therefore, in this course we will examine the historiographical tradition to see how historians have used data and visualization to understand the past. And we will work together to apply these methods to a series of datasets in the history of the nineteenth-century United States, with a focus on religion.

Syllabus for “Text Analysis for Historians”

This semester I am teaching an independent study for graduate students on “Text Analysis for Historians.” You can see the syllabus here. It’s an unashamedly disciplinary course. While of course the readings are heavily dependent on the work that is being done in digital humanities or digital literary studies, the organizing principle is whether a method is likely to be useful for historical questions. And the syllabus is organized around a corpus of Anglo-American legal treatises, with readings to frame our work in the context of U.S. legal history.

They are mentioned on the syllabus, but this class draws from syllabi from Ted Underwood, Andrew Goldstone, and Ben Schmidt, and Kellen Funk offered suggestions for the readings.


A Course in Computational Methods and Nineteenth-Century Religious Data

This semester I am teaching a graduate course on Data and Visualization in Digital History. The aim of this course is to teach students how to do the kind of data analysis and visualization that they are likely to do for a dissertation chapter or a journal article. In my way of working, that means the first part of the semester is an introduction to scripting in R, focusing on the grammar of graphics with ggplot2 and the grammar of data manipulation with dplyr and tidyr. Then the second part of the course is aimed at introducing specific kinds of analysis in the context of historical work. My aim is that this course will be the first in a two course sequence, where the second course (colloquially known as Clio 3) will have more programming in R (as opposed to scripting), will have more *nix-craft, will tackle a more advanced historical problem, will possibly cover more machine learning, and will end up creating interactive analyses in Shiny.

There are a few things about the Data and Visualization course that I think are worth mentioning.

First, I’ve been creating worksheets for historical data analysis each week. These worksheets tend to demonstrate some technique, then ask students to build up an analysis step by step. The questions within each worksheet range in difficulty from the rote and mechanical to the very difficult. While for now these worksheets are aimed at this class in particular, I intend over time to write worksheets like these for any topic in R I end up teaching. I’m rather pleased with these worksheets as a method of teaching data analysis by example.1 If I’m judging my students’ initial reactions correctly, they are also finding them helpful, if rather difficult at times.

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