Introducing “A Three-Step Guide to Training Computational Social Science Ph.D. Students for Academic and Non-Academic Careers”
As D-Lab alumni, we are excited to introduce our pre-print “A Three-Step Guide to Training Computational Social Science PhD Students for Academic and Non-Academic Careers.” Alongside a talented and multidisciplinary group of junior scholars, we aim to demystify the “hidden curriculum” of graduate computational social science programs. We hope that this guide becomes useful for helping students develop the skills and expertise necessary to work as computational social scientists, and navigate their graduate programs that might not formalize such pathways. Critically, we hope this guide is helpful for students interested in pursuing a wide range of careers both within and outside of academia.
Part of our motivation for writing this paper was to formalize much of what we learned during our time in D-Lab. We organize our guide into three broad steps: (1) learning data science skills, (2) building a data science portfolio, and (3) connecting with other computational social scientists. Many of our suggestions about specific skills and workshops are drawn from our experience teaching hundreds of D-Lab workshops in programming basics, machine learning, text analysis, and more. D-Lab also fostered an interdisciplinary learning community where we could meet other students interested in computational social science and explore, learn, and apply new computational tools and techniques together. We argue that such inclusive institutions are going to be critical for helping graduate departments support computational training and research.
We hope you enjoy the preprint, and welcome any feedback and suggestions! We are especially happy to share it with the D-Lab community as it has inspired our collaboration and insights.
About the Authors:
Aniket Kesari was a postdoc and data science fellow at D-Lab. He is currently a research fellow at NYU’s Information Law Institute, and will join the faculty of Fordham Law School in 2023. His research focuses on law and data science, with particular interests in privacy, cybersecurity, and consumer protection.
Jae Yeon Kim was a senior data science fellow at D-Lab. Jae Yeon is a research affiliate at the SNF Agora Institute and P3 Lab at Johns Hopkins University. In 2023, he will join the Code for America as a senior data scientist. He studies computational social science and civic data science, focusing on marginalized populations.