Authors: Aniket Kesari, Jae Yeon Kim, Sono Shah, Taylor Brown, Tiago Ventura, and Tina Law
Publication: PS: Political Science & Politics
Date: September 2023
Abstract:
Social scientists with data science skills increasingly are assuming positions as computational social scientists in academic and non-academic organizations. However, because computational social science (CSS) is still relatively new to the social sciences, it can feel like a hidden curriculum for many Ph.D. students. To support social science Ph.D. students, this article is an accessible guide to CSS training based on previous literature and our collective working experiences in academic, public-, and private-sector organizations. We contend that students should supplement their traditional social science training in research design and domain expertise with CSS training by focusing 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 practical recommendations for departments and professional associations to better support Ph.D. students.
Link: Training Computational Social Science Ph.D. Students for Academic and Non-Academic Careers