Program Overview

Computational Social Science Training Program (CSSTP)

The CSSTP is a new two-year multi-disciplinary training program in advanced data analytics for predoctoral students in the social and behavioral sciences. CSSTP aims to prepare social science researchers to tackle the complex health problems prioritized by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), including maternal and child health, adolescent health, pubertal timing, mental health, health disparities, and the social determinants of health. The training faculty includes 21 social scientists who have exemplary records of developing and applying novel statistical methods to health-related social/behavioral science problems, as well as 13 data scientists who are leading figures in the foundations of mathematics, statistics/biostatistics, and computer science.

Additional Resources/Opportunities

  • Computational Social Science Workshop. Faculty and trainees give and receive peer mentoring, focus on professional development, discuss new research articles, are supported in paper writing and article submission, and receive feedback on ongoing research.
  • Professional development activities. These include the Computational Social Science Annual Meeting to present preliminary work and receive feedback, and data science conferences to share research and begin building a professional network.
  • Responsible Conduct of Research Training. This training involves both principles and tools and emphasizing reproducibility in research, at multiple points in the training program and integrated into other primary components, including the CSS core course, CSS Workshop, and internship.
  • Joint mentorship by both social science and data science training faculty.

ACADEMIC OVERVIEW

Curriculum

  • A year-long course in computational social science that prepares trainees to employ advanced data analytic methods.
  • Two data science elective courses selected from existing courses to develop deep specialized knowledge and skills.
  • Data science research internships in UC Berkeley faculty labs and/or industrial labs.