Courses  /  Spring 2025
HIST 8500 · GRADUATE SEMINAR

Methods in Digital History I

MEETS
Monday and Wednesday · 4-5:15pm
LOCATION
Watt Center 333
CREDITS
3
TERM
Spring 2025
INSTRUCTOR
Dr. Amanda E. Regan
aeregan@clemson.edu
Hardin Hall 004
OFFICE HOURS
My office hours are flexible and you can schedule a time to meet with me. I am generally on campus Monday through Wednesday. Please be sure your failure journal is up to date when scheduling an appointment. Book an appointment.

Course Overview

Welcome to History 8500, Methods in Digital History I. This course in computational history will teach you to create, manipulate, explore, and visualize historical data with the goal of advancing historical arguments.

This course will proceed in roughly 3 units. In the first students will learn to navigate their computer using the command line, learn file system structure and basic server architecture, and will learn how to use GitHub to ensure reproducible datasets and code. Students will also be introduced to static websites and will begin a semester long project of building a personal website and portfolio in the static website software Hugo. The next unit focuses on the basics of the programming with an emphasis on computational thinking skills and the foundational patterns behind every programming language. Finally, in the last half of our course, we will survey various methods within computational history and ask ourselves, what kinds of historical questions do these methods make possible? Each week will focus on a new method – visualization, mapping, and several forms of text analysis. By the end of this course, you’ll have gained a basic familiarity and competency all of these approaches, but more importantly you’ll understand how to ask and answer historical questions using the computational methods that are useful to you. While not required, it is best to take this course alongside History 8550: Theories of Digital Analysis which has been designed to complement the topics in this class and provide a theoretical and scholarly underpinning for the methods we learn in class.

Why R?

There are numerous languages we could learn in this course, but we’ll focus on R which is common in the field of digital history as well as data science. With roots in statistical analysis, R is a high-level programming language (meaning further from machine code or really complicated languages like C++). However, more important than the specific language are the programming fundamentals you will learn. After this class you may decide that Python, PHP, Go, or any other number of programming languages better suits your needs based on the historical questions you want to ask. Learning these fundamentals will allow you to pivot as needed and learn new languages, syntaxes, and tools.

A Note on Data and Sources for this Class

For the most part, I’ve provided datasets for all of the assignments in this class so that you don’t have to worry about learning the material and creating a historical dataset that will work with that approach. The only exception to this is the assignment on dataset creation but I will provide suggested primary source repositories in case you don’t yet have a set of sources in mind. However, you are welcome (and encouraged) to use your own data for any assignment. Doing so will only further prepare you for your future classes in digital history and for your dissertation.

Learning goals:

At the conclusion of this course you will:

  • Use GitHub to create repositories, make commits, and manage code following reproducible research best practices.
  • Understand the basic computational patterns in programming.
  • Be able to use documentation for programming languages and libraries to learn skills as they become applicable to your work.
  • Be able to create historical data from primary sources.
  • Understand how to clean, organize, manipulate, and document that data.
  • Understand how to employ basic computational methodologies like text analysis, topic modeling, word vector analysis, and data maninpulation.
  • Create basic static visualizations to communicate your work and methods to other historians.
Required Texts:
  • A note about texts for this course: There is no need to purchase texts for this course as they are all freely available online or available through the Clemson Library. However, if you prefer hard copies or would like a reference guide, I highly recommend purchasing a copy of Hadley Whickham’s R for Data Science which is an invaluable reference guide.
  • Texts:
    • Whickham, Hadley, R for Data Science, 2nd edition, Ingram Publishers, 2023. Open Access Edition
    • Healy, Kieran, Data Visualization: A Practical Introduction, Princeton University Press, 2019. Open Access Edition

Assignments & Grades

AssignmentPercentage of Grade
Worksheets and Other Coding Assignments40%
Portfolio and Digital Presence15%
Participation, Failure Journal, & Weekly Responses20%
Dataset and Method Proposal (Final Project)25%
  • Worksheets and Other Weekly Coding Assignments (40%): Most weeks you will complete a worksheet that is based on the method for the week. With a few exceptions, these worksheets will be due the week after we discuss that method. Worksheets will be Quatro documents that blend code and prose. In these worksheets you’ll be asked to practice various concepts with a provided dataset. These worksheets will contain a mix of problems ranging from easy to very hard. There is not an answer key that I’ll grade from and many questions will have multiple ways to answer the question. The goal here is to practice the methodology at hand. It’s one thing to read about the methodology and read the documentation for the associated R packages, but it’s another thing to actually apply that methodology to historical sources and ask questions of it. So, in other words, the goal of these worksheets is twofold. First, it is to grasp the basic programming concepts related to the methodology. Second, it is to understand what types of historical questions can be asked of that method and what kinds of arguments or interpretations it makes possible. There are a total of seven worksheets across the semester and I will provide supplementary coding assignments when a worksheet is not due.
  • Portfolio and Digital Presence (15%): To build a successful digital history project, its necessary to communicate findings via the web. Therefore students will need to know a baselevel of website design. In this class we will learn to build sites using the static site generator Hugo. We will spend two weeks learning this technology and then students will work on their own, over the course of the semester to build a digital website and portfolio for themselves using this technology.
  • Tech Table Participation, Failure Journal & Weekly Responses (20%):
    • Each week our Wedensday class will be a “Tech Table.” This is a student driven class session where we will troubleshoot concepts from the week’s topic and worksheet or assignment. For each tech table students will need to have made a reasonable start on the assignment for the week and will come to class ready to discuss potential issues or errors. It is unacceptable to show up to class without having started the assignment so you should plan your time each week accordingly. Students must submit questions about the course material to the Slack channel by 10am on the day of the tech table. The Failure Journal, a log and reflection of your work for this class as well as notes on troubleshooting resources, strategies, and attempts is due each Monday prior to class unless noted otherwise on the schedule.
    • A note on AI: The world of coding is at a turning point and AI tools have changed the landscape significantly. Students will be introduced to AI tools halfway through the semester. However, it is crucial that in the first half of the semester our focus be on learning the logic and underlying processes behind programming concepts. You must learn to think computationally before you can make the most of AI programming tools. Therefore, any use of AI in the first half of the semester will result in an automatic failure on the assignment and I will report you to the graduate school for academic dishonesty. Students may not use AI until expressly given permission by the professor. When students do begin using AI programming tools, they must log their use and reflect on it in their failure journals.
  • Dataset Proposal (25%): At the conclusion of the semester each of you will write a dataset proposal for a digital history project. Further details will be provided in class but students should think about the process of creating a dataset and the decisions that are involved in building one. This assignment will ask you to take a body of historical sources and think about how it might be turned into a dataset that you would use a digital method to analyze. Students should address questions like: What historical question is the dataset centered around? What is the dataset based on? What information will be captured and how will it be stored? Will you be importing external data? (i.e. Historical place names from an existing dataset or information about a person’s term in office.) If so, where will that information come from? Who owns it? What are the methods behind the data collection design and process? Further details and guidelines will be provided in class.

Grading Scale:

  • A (93-100)
  • A- (90–92)
  • B+ (87–89)
  • B (83–86)
  • B- (80–82)
  • C+ (77–79)
  • C (73–76)
  • C- (70–72)

Schedule

Wed., Jan. 8
  • Topics:
    • Introductions
  • Reading, Tutorials, and Videos (to be completed before class):
  • Due by 4pm to canvas:
    • Github Username
    • Tech Fluency Survey
Mon., Jan. 13
Wed., Jan. 15
Mon., Jan. 20
  • No Class, MLK Day
Wed., Jan. 22
  • Tech Table: GitHub and the Commandline
  • Due:
    • Github Questions to Slack by 10am
    • Create a git repository. Inside the git repository should be:
      • A folder called Spring2025
      • 3 markdown (.md) documents, each with the title of a class you are taking this semester. i.e. Hist8500.md
    • Your repository should have no less than 4 commits, one after the creation of each of these documents.
    • Next, create a second branch, titled Fall2024. In this branch you should have all the documents from the previous set. However, you should add a folder called Fall2024 and add 3 markdown files for each class you took last semester.
    • Submit the link to this repository to canvas by 4pm
Mon., Jan. 27
  • Topic:
    • Hugo: Setting up a Personal Website and Portfolio
  • Reading, Tutorials, and Videos (to be completed before class):
    • Giraffe Academy, Hugo Series Watch videos 1-7, 11 and 23.
  • Due by 4pm to canvas:
    • GitHub Pull Request Assignment
Wed., Jan. 29
  • Tech Table
  • Due:
    • Github Questions to Slack by 10am
Mon., Feb. 3
  • Topic:
    • Hugo: Templating, Theming, and other Things to Know
  • Reading, Tutorials, and Videos (to be completed before class):
  • Due by 4pm to canvas:
    • Link to your hugo site github repository. You need to make reasonable progress on getting the basics of a site setup.
Wed., Feb. 5
  • Topic:
    • Tech Table: Hugo
  • Due:
    • Github Questions to Slack by 10am
Mon., Feb. 10
  • Topic:
    • R Basics
    • Introduction to Quarto, R scripts, and R projects
  • Reading, Tutorials, and Videos (to be completed before class):
    • Hadley Wickham & Garrett Grolemund. R for Data Science: Chapters 2 (Basics), 6 (Workflow: Scripts and Projects), 7 (Data Import), and 28 and 29 (Quarto).
    • Bruno Rodrigues. Modern R, Chapter 1: Getting to know R Studio
Wed., Feb. 12
  • Topic:
    • Tech Table
  • Due:
    • Github Questions to Slack by 10am
    • A reasonable start on Worksheet 1
Mon., Feb. 17
Wed., Feb. 19
  • Topic:
    • Tech Table
  • Due:
    • Github Questions to Slack by 10am
    • A reasonable start on Worksheet 2
Mon., Feb. 24
Wed., Feb. 26
  • Topic:
    • Tech Table
  • Due:
    • Using the principles of Tidy Data create a well structured csv file before class. You should use either a primary source dataset that you have already, or one of the primary source sets linked below.
    • Then take your dataset and think about how it might look different if it was relational. Sketch a database schema wireframe that describes what tables it would have and how they relate to one another.
    • Upload the dataset, a brief description of it, and an image of your database schema to the Slack thread by 4pm.
Mon., Mar. 3
Wed., Mar. 5
  • Topic:
    • Tech Table
  • Due by 4pm:
    • Github Questions to Slack by 10am
    • A reasonable start on Worksheet 3
Mon., Mar. 10
Wed., Mar. 12
  • Topic:
    • Tech Table
    • Reading and assignments TBA
Mon., Mar. 17 and Wed., March 19
  • Spring Break
Mon., Mar. 24
  • Topic:
    • Visualizations in ggplot and the Grammar of Graphics
  • Reading, Tutorials, and Videos (to be completed before class):
  • Due by 4pm to canvas:
    • Web Scraping Script
Wed., Mar. 26
  • Topic:
    • Tech Table
  • Due:
    • Github Questions to Slack by 10am
    • Reasonable start on Worksheet 4
Mon., Mar. 31
Wed., Apr. 2
Mon., Apr. 7
Wed., Apr. 9
Mon., Apr. 14
Wed., Apr. 16
  • Topic:
    • Tech Table
  • Due:
    • Github Questions to Slack by 10am
    • Reasonable Start on Word Vector Assignment
Mon., Apr. 21
Wed., Apr. 23
Tues., Apr. 29 7-9:30pm



Policies & Procedures

Please note that this syllabus may be updated online as necessary. The online version of this syllabus is the only authoritative version.

Late Work

Due dates for all assignments are listed on the course syllabus and in the schedule for the class. Because of the technical nature of this class it is essential that you keep up with the worksheets. If you miss one worksheet the others will be much harder because you will have missed key concepts. Unless otherwise stated, assignments are due on the day listed on the syllabus and canvas. If you submit an assignment late, I will deduct 10% for every day that it is late. Assignments submitted more than 7 days after the due date will not be accepted.

Classroom Conduct

In order to learn, we must be open to the views of people different from ourselves. In the time we share together over the semester, please honor the uniqueness of your fellow classmates and appreciate the opportunity we have to learn from one another. Please respect each other’s opinions and refrain from personal attacks or demeaning comments of any kind. Anyone who engages in hostile or antagonistic rhetoric will be asked to leave the classroom immediately.

Academic Integrity

As members of the Clemson University community, we have inherited Thomas Green Clemson’s vision of this institution as a “high seminary of learning.” Fundamental to this vision is a mutual commitment to truthfulness, honor, and responsibility, without which we cannot earn the trust and respect of others. Furthermore, we recognize that academic dishonesty detracts from the value of a Clemson degree. Therefore, we shall not tolerate lying, cheating, or stealing in any form.

All infractions of academic dishonesty by undergraduates must be reported to Undergraduate Studies for resolution through that office. In cases of plagiarism instructors may use the Plagiarism Resolution Form.

See the Graduate Academic Integrity Policy website for additional information and the current Clemson Graduate Handbook for the policy.

Please keep in mind that if you are copying and pasting text that you did not write yourself, you might be plagiarizing. If you are using copied text, whether pasted or retyped manually, you must be sure to accurately cite the information. Text is accurately cited when: 1) pasted text is surrounded by quotation marks or offset as a block quote and 2) the pasted text is attributed to its author and source and 3) the pasted text is cited in a footnote, endnote, or bibliography.

AI-based coding assistance is rapidly changing the way that programmers work. In the second half of the semester, we will treat AI-based assistance, such as ChatGPT or Github Copilot, the same way we treat collaboration with other people: you are welcome to talk about your ideas and work with other people, both inside and outside the class, as well as with AI-based assistants. However, all work you submit must be your own. You should never include in your assignment anything that was not written directly by you without proper citation. Including anything you did not write in your assignment without proper citation will be treated as an academic misconduct. Any use of AI before the instructor expressly permits its use will be treated as academic misconduct.

Student Accessibility Services

Clemson University values the diversity of our student body as a strength and a critical component of our dynamic community. Students with disabilities or temporary injuries/conditions may require accommodations due to barriers in the structure of facilities, course design, technology used for curricular purposes, or other campus resources. Students who experience a barrier to full access to this class should let the instructor know and make an appointment to meet with a staff member in Student Accessibility Services as soon as possible. You can make an appointment by calling 864-656-6848, by emailing studentaccess@lists.clemson.edu, or by visiting Suite 239 in the Academic Success Center building. Appointments are strongly encouraged – drop-ins will be seen if at all possible, but there could be a significant wait due to scheduled appointments. Students who have accommodations are strongly encouraged to request, obtain and send these to their instructors through their AIM portal as early in the semester as possible so that accommodations can be made in a timely manner. It is the student’s responsibility to follow this process each semester.
You can access further information at the Student Accessibility website. Other information is at the university’s Accessibility Portal.

Commitment to Diversity

“Clemson University aspires to create a diverse community that welcomes people of different races, cultures, ages, genders, sexual orientation, religions, socioeconomic levels, political perspectives, abilities, opinions, values and experiences.” - The Clemson University Title IX statement regarding non-discrimination

Clemson University is committed to a policy of equal opportunity for all persons and does not discriminate on the basis of race, color, religion, sex, sexual orientation, gender, pregnancy, national origin, age, disability, veteran’s status, genetic information or protected activity in employment, educational programs and activities, admissions and financial aid. This includes a prohibition against sexual harassment and sexual violence as mandated by Title IX of the Education Amendments of 1972. This Title IX policy is located on the Campus Life website. Ms. Alesia Smith is the Clemson University Title IX Coordinator, and the Executive Director of Equity Compliance. Her office is located at 223 Brackett Hall, 864.656.0620. Remember, email is not a fully secured method of communication and should not be used to discuss Title IX issues.

Emergency Preparedness

Emergency procedures have been posted in all buildings and on all elevators. Students should be reminded to review these procedures for their own safety. All students and employees should be familiar with guidelines from the Clemson Police Department. Visit here for information about safety.

Clemson University is committed to providing a safe campus environment for students, faculty, staff, and visitors. As members of the community, we encourage you to take the following actions to be better prepared in case of an emergency:

Questions about the course? Get in touch.
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