In a new article in PNAS, BIDS Research Scientist Karthik Ram and his co-authors explore the new concept of Hack weeks as a model for data science education and collaboration, and their successes in implementing it as a platform for improving participant engagement in interdisciplinary research.
In almost all scientific disciplines, the acquisition and analysis of experimental data is requiring scientific communities to adapt to more urgent time scales for knowledge transfer. To generate new opportunities that foster exchange of ideas and computational best-practices across disciplines, hack weeks have emerged as a effective tool for fostering these exchanges and for providing training in modern data analysis workflows. According to Ram, “Hackweeks fill a very significant gap in data science education. It is very hard to find such a combination of project based learning and pedagogy for modern data science elsewhere.”
Through a combination of tutorials in state-of-the-art methodology, peer-learning and project work presented in an collaborative environment, hack weeks provide an easy-to- implement and fairly low-cost method to introduce new technologies and methods on much shorter time scales than traditional teaching efforts. While the authors focus primarily on hack weeks in scientific fields, they suggest that concept could be extended to other areas, including the social sciences, the humanities, as well as music and art; and in fact that the general format of Hack Weeks could be useful in any area where useful tools can be learned in short tutorials, where results and outcomes can be produced on the timescale of a few days, and that would benefit from collaborative approaches that cross traditional boundaries.
Hack weeks are still a novel idea, and estimating the long-term impact of these events within the scientific communities they serve will require follow-up to assess their effects on collaboration networks, career outcomes and adoption of new methods.
Hack weeks as a model for data science education and collaboration (PDF 786 KB)
August 20, 2018 | PNAS
Daniela Huppenkothen, Anthony Arendt, David W. Hogg, Karthik Ram, Jake VanderPlas, and Ariel Rokem
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