A Three-Step Guide to Training Computational Social Science Ph.D. Students for Academic and Non-Academic Careers

In recent years, social scientists with data science skills have gained positions in academic and non-academic organizations as computational social scientists who blend skillsets from data science and social science. Yet as this trend is relatively new in the social sciences, navigating these emerging and diverse career paths remains ambiguous. We formalize this hidden curriculum by providing a step-by-step guide to graduate students based on our collective experiences as computational social scientists working in academic, public, and private sector organizations. Specifically, we break down the computational social science (CSS) professionalization process into three steps: (1) learning data science skills; (2) building a portfolio that focuses on using data science to answer social science questions; and (3) connecting with other computational social scientists. For each step, we identify and elaborate on core competencies and additional useful skills that are specific to the academic and non-academic job markets. Although this article is not exhaustive, it provides a much-needed guide for graduate students, as well as their faculty advisors and departments, to navigate the growing field of computational social science. By sharing this guide, we hope to help make computational social science professionalization more systematic and accessible.