Training Computational Social Science PhD Students for Academic and Non-Academic Careers

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 PhD students. To support social science PhD 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 PhD students.

Working Interinstitutionally to Apprentice Doctoral Students in Mathematics Education Research

“…This includes the capacity not only to read and implement published research into policy and practice but also to do research studies that are specific to institutions' needs. Disciplines may differ regarding how many doctorate-holders who work outside of academia go into research or into other positions (e.g., leadership), but research skills in the social sciences are among those required and valued in nonacademic positions (Hancock, 2023;Kesari et al, 2024; see also Henkel & Kogan, 1993;Mills, 2009;Morrison et al, 2011). Furthermore, as the competition for regular faculty positions in universities becomes more intense, research productivity and potential for acquiring research funding will probably matter more (Peterson et al, 2023).…”