The major assignment for this course is a research project in which you find or create a dataset that allows you to investigate some sort of historical topic. You will then use Python to process and analyze the data in order to present an argument, interpretation, or narrative based on that analysis. You are free to choose any topic or dataset that you want, but it must be historical in nature (broadly defined). (See this example of a former student’s project.) The assignment is scaffolded across several steps.

Research Project Proposal

Submit a research proposal over Canvas that addresses the following questions.

  • Name
  • Title of your project
  • What research topic are you interested in?
  • Description of Dataset: What kind of information does it contain? Are you going to be transcribing it yourself or is it already in a machine-readable format? How are you going to access it?
  • Ideas for analysis: What research questions do you want to answer and how can you use this data to answer them?

Note: if you need ideas for where to find a dataset, take a look at this guide to get started.

Due on Canvas by Sunday, April 13th by 11:59PM

Research Project Draft

Your full research project will take the form of a Jupyter Notebook containing written analysis and code, all rendered on a page or set of pages on your portfolio website (we will be going over instructions on how to do this). You should include:

  • Title, Author, and Date: The title of your project, your name, and date of publication (I would recommend headers or other ways of visually distinguishing these)
  • Generative AI Disclaimer: If you used ChatGPT, Claude, etc. in your research, add a short disclaimer (ex. “Note: Generative AI was used to help generate some of the code in this project.”)
  • Introduction of the questions you sought to answer about your data and any context that you think your reader will need to understand the data, including existing scholarship or research on the topic
  • Methodological discussion of how the data was collected, processed, and analyzed
  • Main analysis of the dataset: a clearly communicated argument, interpretation, or narrative supported by compelling evidence and examples; appropriate visualizations of your dataset
  • Conclusion: the larger cultural context of this dataset; ethical concerns or issues around its collection or application
  • Link to project’s Github Repository: provide a link to the project’s Github repository
  • Bibliography: list of existing scholarship along with sources of your data

Submission:

Due on Canvas by Monday, May 5th or Wednesday, May 7th by 11:59PM

Presentation of Research Project Draft

During the last week of the semester you will give a 5-minute presentation to your classmates that walks through your dataset and major analytical findings. Treat this as both an opportunity to share what you’ve accomplished and a chance to solicit feedback on specific pieces of your project. Do NOT try to cover everything about your project. Instead, focus on giving a high-level introduction followed by a few examples or areas of the project that you would like feedback on.

Due Tuesdsay, May 6th or Thursday May 8th in class

Peer Review of Research Project Draft

You will be assigned one of your classmates’ projects to review in more depth. As part of the review, you will read their project analysis and provide qualitative feedback over Canvas using the following criteria:

Introduction

  • How well does the author introduce their topic, their data, and their research questions?
  • How well does the project engage with existing research or perspectives?
  • Is anything confusing? What additional information could they include? Is there anything they could remove to make it more succinct?

Data and Methodology

  • How clearly is the data processing and analysis explained for someone unfamiliar with the project?
  • What strengths or limitations do you notice in the way the author handled their data?

Interpretation and Analysis

  • What insights or arguments does the author draw from the data? How compelling are these insights?
  • How effectively do they use evidence - both quantitative and qualitative - to support their interpretation?
  • Where could the analysis go deeper, or where might the interpretation be expanded or refined?

Code

  • Does their code directly tie into their written analysis and interpretation? Are they properly explained for the reader in the preceding written paragraph or in comments within the code cell?
  • How effective are any visual outputs (maps, charts, etc.)? What suggestions do you have to improve these?
  • Are there opportunities to simplify, streamline, or improve the code? Do you notice any other issues or areas for improvement?

Writing and Communication

  • How clear and engaging is the writing? Is the language accessible and understandable to a non-expert audience?
  • Are there issues with typos, confusing sentence construction, etc.?
  • How well are the sections organized — is there a logical flow from introduction to conclusion?
  • Does the author effectively use things like headers, section breaks, bold/italics, etc. to organize and communicate their points?

Documentation and Transparency

  • Does the author include a bibliography that documents their research?
  • If applicable, does the author include a disclaimer about their use of generative AI?
  • Does the author include a link to their project’s GitHub repository? Is their GitHub repository well-organized and have enough documentation for a user to understand it?

Due on Canvas Friday, May 9th by 11:59PM

Research Project (Final)

This will take the same format as the draft outlined above. Note that I will be assessing your final version of the project not only on its overall quality, but also on the quality of your revisions and how well you took into account feedback from your peers and myself.

Due on Canvas by Thursday, May 15th by 11:59PM