数据科学的Python教学:模块化和互动课程的协作开发

M. Duda, Kelly L. Sovacool, Negar Farzaneh, V. Nguyen, Sarah E. Haynes, Hayley Falk, Katherine L. Furman, Logan A. Walker, Rucheng Diao, M. Oneka, Audrey C. Drotos, Alana Woloshin, Gabrielle A. Dotson, April Kriebel, Lucy Meng, Stephanie N. Thiede, Z. Lapp, B. Wolford
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引用次数: 2

摘要

我们是密歇根大学的生物信息学学员,我们创建了“编程女孩”在当地的分会,为高中女生提供一个有趣和支持性的环境,让她们学习编程的力量。我们的目标是通过实时编码和动手实践来涵盖基本的编码主题和数据科学概念。然而,我们找不到完全满足我们需求的资源。因此,在过去的三年中,我们使用Jupyter笔记本开发了一套课程和教学格式,以有效地教授数据科学入门Python。这种方法受到The Carpentries组织的启发,使用小型课程,然后是独立的实践时间来强化编码概念,并在使用真实数据的数据科学顶点项目中达到高潮。我们相信我们的开放课程对更广泛的教育界来说是一种宝贵的资源,我们希望教育工作者能够使用和改进我们的课程、实践问题和教学最佳实践。任何人都可以在GitHub上贡献我们的教育材料。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Teaching Python for Data Science: Collaborative development of a modular & interactive curriculum
We are bioinformatics trainees at the University of Michigan who started a local chapter of Girls Who Code to provide a fun and supportive environment for high school women to learn the power of coding. Our goal was to cover basic coding topics and data science concepts through live coding and hands-on practice. However, we could not find a resource that exactly met our needs. Therefore, over the past three years, we have developed a curriculum and instructional format using Jupyter notebooks to effectively teach introductory Python for data science. This method, inspired by The Carpentries organization, uses bite-sized lessons followed by independent practice time to reinforce coding concepts, and culminates in a data science capstone project using real-world data. We believe our open curriculum is a valuable resource to the wider education community and hope that educators will use and improve our lessons, practice problems, and teaching best practices. Anyone can contribute to our educational materials on GitHub.
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