Nick Eubank is an Assistant Research Professor in the Duke Social Science Research Institute (SSRI), where he studies a range of topics related to political accountability, include gerrymandering, social networks, election administration and race and incarceration.
Eubank is a faculty member and Admissions Chair for the Duke Master in Interdisciplinary Data Science (MIDS), where he strives to provide students of all backgrounds with practical, transferable, data science skills. In particular, he developed and teach two courses that are part of the first-year MIDS curriculum. The first is Practical Data Science, a flipped-classroom, exercise-focused course designed to give students practical experience wrangling and analyzing messy, real-world data using the tools of a professional data scientist (e.g. the Python data science stack, git and github, Jupyter, VS Code, etc.). The second is Unifying Data Science. In the first portion of the two-part course, students are introduced to causal inference (including the potential outcomes framework, A/B testing, differences-in-differences, and analytic concepts like internal and external validity); the second portion of the course employs a project-based learning strategy to teach students how to approach and develop a full data science project through a question-first approach and backward design.
In addition, in an effort to make data science accessible to students outside of the MIDS program, he also runs a Computations Methods for Social Scientists bootcamp for incoming social science graduate students from Political Science, Sociology, and the Nicholas School for Environmental Policy.