Computational Social Science Methods (PA397C|INF385T)

Course description

  • Instructor: Ji Ma ([email protected])
  • Time and location: 2024 Fall, Tuesday 9:00AM-12:00PM, SRH 3.314.
  • Office hour: TBD.

This course is academic and research oriented, it introduces and contextualizes computational methods from a social science research design perspective. The first part of this course (w1–w3) gives you an overview of this course, how to use high-performance cloud computing resources, and how to analyze computational methods from a research design perspective. The second part (w4–w13) will analyze different computational methods according to their roles in social science research. Students will present their final research project in the last week. Bilingual or multilingual language ability is a plus. Programming is an essential part of this course but not the purpose and will not be taught. We will be coding for social good.

Prerequisites

The course has demanding prerequisites; therefore, you probably need to work on the prerequisites over the winter break if you are highly motivated. All registrations need to be approved by the instructor in late 2023. Please provide either (1) your code scripts, or (2) a committed learning plan during the winter break, to me to evaluate your preparedness.

Recommended (not required) textbooks / e-books

These books give you a good theoretical understanding and are very useful in research design.

  • [GRS] Grimmer, Justin, Margaret E. Roberts, and Brandon M. Stewart. 2022. Text as Data: A New Framework for Machine Learning and the Social Sciences. Princeton, New Jersey Oxford: Princeton University Press.
  • [SJ] Scott, John. 2017. Social Network Analysis. Fourth edition. Thousand Oaks, CA: SAGE Publications Ltd. (different versions are fine)

These books/sources introduce more technical and hands-on details.

  • [GS] Gentzkow, Matthew, and Jesse M. Shapiro. 2014. Code and Data for the Social Sciences: A Practitioner’s Guide. https://web.stanford.edu/~gentzkow/research/CodeAndData.pdf.
  • [JM] Jurafsky, Daniel, and James H. Martin. 2022. Speech and Language Processing. 3rd draft. https://web.stanford.edu/~jurafsky/slp3/. (the authors generously made their book publicly available, check their website and use the latest version)
  • NetworkX (the package’s documentation and the references cited are the best place to start in terms of technical details)

Presentations and final projects from previous semesters

Grading

Assignments (TBD)

  • A >= 95%, A- >= 90
  • B+ >= 87%, B >= 83%, B- >= 80%
  • C+ >= 77%, C >= 73%, C- >= 70%
  • D+ >= 67%, D >= 63%, D- >= 60%

Policies

  • Mental health: The instructor urge students who are struggling for any reason and who believe that it might impact their performance in the course to reach out if they feel comfortable. This will allow the instructor to provide any possible resources or accommodations. If immediate mental health assistance is needed, call the Counseling and Mental Health Center (CMHC) at 512-471-3515. You may also contact Bryce Moffett, LCSW (LBJ CARE counselor) at 512-232-4449 or stop by her office hours-Wednesday 1-2 pm SRH 3.119. Outside CMHC business hours (8a.m.-5p.m., Monday-Friday), contact the CMHC 24/7 Crisis Line at 512-471-2255.
  • University Policies
  • By taking this course (either for credit or auditing), you automatically authorize the instructor to use or cite the contents created by you for this course in the instructor’s working book project. Appropriate academic principles of attribution and integrity will be followed.
  • License for Open Education: This syllabus and all course content on this website created by the instructor, TA, and students are licensed under the Creative Commons Attribution-NonCommercial 4.0 International License.

Acknowledgements


Theme by pro-panda