Hack Weeks as a model for Data Science Education and Collaboration
Daniela Huppenkothen, Anthony Arendt, David W. Hogg, Karthik Ram, Jake, VanderPlas, Ariel Rokem

TL;DR
Hack weeks are effective, low-cost events that enhance data analysis skills, foster interdisciplinary collaboration, and promote best practices in scientific research through tutorials, peer-learning, and project work.
Contribution
This paper introduces the hack week model as a novel approach for data science education and collaboration across scientific disciplines.
Findings
Hack weeks successfully foster collaboration and knowledge exchange.
Participants report improvements in research and career development.
Hack weeks are a low-cost, effective tool for improving data analysis literacy.
Abstract
Across almost all scientific disciplines, the instruments that record our experimental data and the methods required for storage and data analysis are rapidly increasing in complexity. This gives rise to the need for scientific communities to adapt on shorter time scales than traditional university curricula allow for, and therefore requires new modes of knowledge transfer. The universal applicability of data science tools to a broad range of problems has generated new opportunities to foster exchange of ideas and computational workflows across disciplines. In recent years, hack weeks have emerged as an effective tool for fostering these exchanges by providing training in modern data analysis workflows. While there are variations in hack week implementation, all events consist of a common core of three components: tutorials in state-of-the-art methodology, peer-learning and project work…
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