Designing Data Science Workshops for Data-Intensive Environmental Science Research. (1st January 2021)

• Record Type: Journal Article
• Authors: Theobold, Allison S.; Hancock, Stacey A.; Mannheimer, Sara
• Is Part Of: Journal of Statistics and Data Science Education. Volume 29(2021)Supplement 1
• Page Start: S83
• Page End: S94
• Publication Date: 2021-01-01
• Subjects: Data science -- Data visualization -- Data wrangling -- Environmental science -- R -- Reproducible research
• DOI: 10.1080/10691898.2020.1854636
• Languages: English
• ISSNs: 2693-9169

Abstract

Abstract: Over the last 20 years, statistics preparation has become vital for a broad range of scientific fields, and statistics coursework has been readily incorporated into undergraduate and graduate programs. However, a gap remains between the computational skills taught in statistics service courses and those required for the use of statistics in scientific research. Ten years after the publication of "Computing in the Statistics Curriculum, " the nature of statistics continues to change, and computing skills are more necessary than ever for modern scientific researchers. In this article, we describe research on the design and implementation of a suite of data science workshops for environmental science graduate students, providing students with the skills necessary to retrieve, view, wrangle, visualize, and analyze their data using reproducible tools. These workshops help to bridge the gap between the computing skills necessary for scientific research and the computing skills with which students leave their statistics service courses. Moreover, though targeted to environmental science graduate students, these workshops are open to the larger academic community. As such, they promote continued learning of the computational tools necessary for working with data, and provide resources for incorporating data science into the classroom.