Training Computational Social Science PhD Students for Academic and Non-Academic Careers ('24 IC2S2 Tutorial)
- Time: 1:30-5 PM (July 17, 2024)
- Venue: Houston: Hall of Flags (K) ('24 IC2S2 schedule)
The goal of this tutorial is to assist graduate students, faculty, and administrators in comprehending the various pathways available for students to enhance their computational social science skills, portfolios, and networks.
This tutorial will particularly benefit social science PhD students by providing them with a better understanding of the career opportunities that are available in computational social science. Additionally, faculty and administrators will be able to find new ways to support these endeavors.
Social scientists with data science skills are increasingly assuming positions as computational social scientists in academic and non-academic organizations. However, as computational social science (CSS) is still relatively new to the social sciences, CSS can feel like a hidden curriculum for many Ph.D. students.
To support social science Ph.D. students, we provide an accessible tutorial for CSS training based on our collective working experiences in academic, public, and private sector organizations. We argue that students should supplement their traditional social science training in research design and domain expertise with CSS training, focused on three core areas:
- (1) learning data science skills;
- (2) building a portfolio that uses data science to answer social science questions; and
- (3) connecting with computational social scientists.
We conclude with some practical recommendations for departments and professional associations to better support Ph.D. students.
The paper form of this tutorial was published in PS: Political Science and Politics, the American Political Science Association’s professionalization journal, and has been viewed 4,615 times and downloaded 1,277 times since August 2023 (as of July 3, 2024).
Table 1: Computational Social Science Professionalization Process (Kesari et al 2024: 102)
- Aniket Kesari (Fordham Law)
- Jae Yeon Kim (Johns Hopkins, ex-Code for America)
- Tiago Ventura (Georgetown, ex-Twitter)
- Tina Law (UC Davis)
- Sarah Shugars (Rutgers)
- Sono Shah (Pew Research Center)
- Soubhik Barari (NORC, Columbia)
The panelists are listed alphabetically.
Each session is organized by a 10-minute presentation, a moderated panel discussion, and an open Q&A.
We intentionally designed the tutorial this way so that the participants could benefit from the short presentations that can guide the following panel and open discussions. Participants can raise their questions and gain information and insights from presenters and other panelists regarding building skills and career paths in computational social science.
- Slides (presenter: Souhbik Barari, moderator: Tiago Ventura)
- Slides (presenter: Jae Yeon Kim, moderator: Aniket Kesari)
- Slides (presenter: Sarah Shugars, moderator: Jae Yeon Kim & Tina Law)