• Instructor: Benjamin Soltoff, Lecturer in Computational Social Science
• Meeting day/time: MW 11:30-1:20pm (Saieh Hall of Economics 247)
• Office hours: Th 12:30-2:30pm (McGiffert House 209)

This course focuses on applying computational methods to conducting social scientific research through a student-developed research project. Students will identify a research question of their own interest that involves a direct reference to social scientific theory, use of data, and a significant computational component. The students will collect data, develop, apply, and interpret statistical learning models, and generate a fully reproducible research paper. We will identify how computational methods can be used throughout the research process, from data collection and tidying, to exploration, visualization and modeling, to the final communication of results. The course will include modules on theoretical and practical considerations, including topics such as epistemological questions about research design, writing and critiquing papers, and additional computational tools for analysis.

• All assignments related to the final project will be turned in via their own public GitHub repositories. You can create this repository here.
• Each problem set will be submitted via pull request to individual repositories (e.g. hw01, hw02, hw03)

Late problem sets will be penalized 1 point for every hour they are late. For example, if an assignment is due on Monday at 11:30am, the following points will be deducted based on the time stamp of the last commit.

If you need any special accommodations, please provide me (Dr. Soltoff) with a copy of your Accommodation Determination Letter (provided to you by the Student Disability Services office) as soon as possible so that you may discuss with me how your accommodations may be implemented in this course.

All readings are required unless otherwise noted. Adjustments can be made throughout the quarter. Be sure to check this repository frequently to make sure you know all the assigned readings.

• Data/methods section
  • Broockman, D. E., & Skovron, C. (2018). Bias in Perceptions of Public Opinion among Political Elites. American Political Science Review, 1-22.
  • Kalla, J. L., & Broockman, D. E. (2016). Campaign contributions facilitate access to congressional officials: A randomized field experiment. American Journal of Political Science, 60(3), 545-558.
• [DLPyR] Deep learning with Python/R
  • Chollet, F. (2017). Deep learning with Python. Manning Publications.
  • Chollet, F. with J.J. Allaire (2018). Deep learning with R. Manning Publications.
• [CR] Booth, W. C., Colomb, G. G., Williams, J. M., Bizup, J., & FitzGerald, W. (2016). The craft of research. University of Chicago press.
  • eBook available through the UChicago library