Motivation

Modern data science finds use in wide-ranging applications in many fields. Data science training involves sufficient grounding in the core components—Applied Mathematics, Statistics and Computer Science. The practice of data science invariably requires sufficient mastery of computing tools in pursuit of research goals.

Increasingly, methodological advances come with computing tools/software implementations that are not always simple or even familiar to the all data scientists. That is often due to the nature of the beast: varied applicability, software choice, and sheer complexity. Examples include techniques for privacy-aware computations, debiasing methods for algorithms, robust inference, causal modeling, to name just a few. Some methods are also bespoke and require practical experience to discern the full applicability and trade-offs involved in utilizing them for research. So there are many data scientists who are aware of such techniques but have never dipped their toes in the water.

Our proposed course draws some inspiration from the data science scholars program, where a selected group of graduate students, from a wide variety of departments but sharing an involvement with data science, have been engaging in wide-ranging discussions. Specifically, the “Data + Donuts” meetings of the scholars, which follow a presentation-plus-discussion format, have resulted in vigorous and helpful interactions. This course builds upon it by exposing participants to novel methodologies along with practical experience with associated software tools.

Unlike traditional courses in statistics or other departments where one might do a data science project as part of the course, the proposed seminar design can give students a practical understanding of data science topics, including cross-discipline considerations.