https://github.com/atheobold/data-science-ws
Designing Data Science Workshops for Data-Intensive Environmental Science Research
https://github.com/atheobold/data-science-ws
Last synced: 6 months ago
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Designing Data Science Workshops for Data-Intensive Environmental Science Research
- Host: GitHub
- URL: https://github.com/atheobold/data-science-ws
- Owner: atheobold
- License: cc0-1.0
- Created: 2020-11-13T02:48:00.000Z (over 5 years ago)
- Default Branch: main
- Last Pushed: 2021-05-12T13:50:03.000Z (about 5 years ago)
- Last Synced: 2025-04-08T16:55:36.071Z (over 1 year ago)
- Language: HTML
- Size: 19.6 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Designing Data Science Workshops for Data-Intensive Environmental Science Research
[Allison Theobold](https://statistics.calpoly.edu/allison-theobold), California Polytechnic University -- San Luis Obispo
[Stacey Hancock](https://math.montana.edu/directory/faculty/1941032/stacey-hancock), Montana State University
[Sara Mannheimer](http://www.lib.montana.edu/directory/1629171/sara-mannheimer), Montana State University
To cite this article:
[Link to Article in the *Journal of Statistics and Data Science Education*](https://www.tandfonline.com/doi/full/10.1080/10691898.2020.1854636)
# 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 paper, 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 the continued learning of
the computational tools necessary for working with data, and provide resources
for incorporating data science into the classroom.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.