Institute for Data-Intensive Research in Astrophysics and Cosmology, Department of Astronomy, University of Washington, Seattle, WA 98195;
Center for Data Science, New York University, New York, NY 10003.
Proc Natl Acad Sci U S A. 2018 Sep 4;115(36):8872-8877. doi: 10.1073/pnas.1717196115. Epub 2018 Aug 20.
Across many scientific disciplines, methods for recording, storing, and analyzing data are rapidly increasing in complexity. Skillfully using data science tools that manage this complexity requires training in new programming languages and frameworks as well as immersion in new modes of interaction that foster data sharing, collaborative software development, and exchange across disciplines. Learning these skills from traditional university curricula can be challenging because most courses are not designed to evolve on time scales that can keep pace with rapidly shifting data science methods. Here, we present the concept of a hack week as an effective model offering opportunities for networking and community building, education in state-of-the-art data science methods, and immersion in collaborative project work. We find that hack weeks are successful at cultivating collaboration and facilitating the exchange of knowledge. Participants self-report that these events help them in both their day-to-day research as well as their careers. Based on our results, we conclude that hack weeks present an effective, easy-to-implement, fairly low-cost tool to positively impact data analysis literacy in academic disciplines, foster collaboration, and cultivate best practices.
在许多科学学科中，记录、存储和分析数据的方法的复杂性正在迅速增加。熟练使用管理这种复杂性的数据科学工具需要新的编程语言和框架的培训，以及沉浸在新的交互模式中，以促进数据共享、协作软件开发以及跨学科交流。从传统的大学课程中学习这些技能具有挑战性，因为大多数课程的设计跟不上数据科学方法的快速变化。在这里，我们提出了黑客周的概念，它是一种有效的模式，可以提供网络和社区建设、最先进的数据科学方法教育以及协作项目工作的机会。我们发现黑客周非常成功地培养了合作，并促进了知识的交流。参与者们自我报告说，这些活动对他们的日常研究和职业发展都有帮助。根据我们的结果，我们得出结论，黑客周是一种有效的、易于实施的、成本相对较低的工具，可以积极影响学术领域的数据分析素养，促进合作，并培养最佳实践。
