{"id":18241238,"url":"https://github.com/atheobold/data-science-ws","last_synced_at":"2026-01-21T01:32:31.887Z","repository":{"id":97000285,"uuid":"312455552","full_name":"atheobold/data-science-ws","owner":"atheobold","description":"Designing Data Science Workshops for Data-Intensive Environmental Science Research","archived":false,"fork":false,"pushed_at":"2021-05-12T13:50:03.000Z","size":20557,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-04-08T16:55:36.071Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"HTML","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"cc0-1.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/atheobold.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2020-11-13T02:48:00.000Z","updated_at":"2024-04-18T15:22:12.000Z","dependencies_parsed_at":"2023-03-11T12:31:44.949Z","dependency_job_id":null,"html_url":"https://github.com/atheobold/data-science-ws","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/atheobold/data-science-ws","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/atheobold%2Fdata-science-ws","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/atheobold%2Fdata-science-ws/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/atheobold%2Fdata-science-ws/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/atheobold%2Fdata-science-ws/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/atheobold","download_url":"https://codeload.github.com/atheobold/data-science-ws/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/atheobold%2Fdata-science-ws/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":28621636,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-01-20T23:49:58.628Z","status":"ssl_error","status_checked_at":"2026-01-20T23:47:29.996Z","response_time":117,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-05T05:41:41.381Z","updated_at":"2026-01-21T01:32:31.870Z","avatar_url":"https://github.com/atheobold.png","language":"HTML","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Designing Data Science Workshops for Data-Intensive Environmental Science Research \n\n[Allison Theobold](https://statistics.calpoly.edu/allison-theobold), California Polytechnic University -- San Luis Obispo\n\n[Stacey Hancock](https://math.montana.edu/directory/faculty/1941032/stacey-hancock), Montana State University \n\n[Sara Mannheimer](http://www.lib.montana.edu/directory/1629171/sara-mannheimer), Montana State University\n\nTo cite this article:  \n\n[Link to Article in the *Journal of Statistics and Data Science Education*](https://www.tandfonline.com/doi/full/10.1080/10691898.2020.1854636)\n\n# Abstract \n\nOver the last 20 years, statistics preparation has become vital for a\nbroad range of scientific fields, and statistics coursework has been readily \nincorporated into undergraduate and graduate programs. However, a gap remains \nbetween the computational skills taught in statistics service courses and those\nrequired for the use of statistics in scientific research. Ten years after the \npublication of \"Computing in the Statistics Curriculum,\" the nature of \nstatistics continues to change, and computing skills are more necessary than \never for modern scientific researchers. In this paper, we describe research on \nthe design and implementation of a suite of data science workshops for \nenvironmental science graduate students, providing students with the skills \nnecessary to retrieve, view, wrangle, visualize, and analyze their data using \nreproducible tools. These workshops help to bridge the gap between the computing\nskills necessary for scientific research and the computing skills with which\nstudents leave their statistics service courses. Moreover, though\ntargeted to environmental science graduate students, these workshops are open to\nthe larger academic community. As such, they promote the continued learning of \nthe computational tools necessary for working with data, and provide resources\nfor incorporating data science into the classroom.\n\n\n\n\u003ca rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc/4.0/\"\u003e\u003cimg alt=\"Creative Commons License\" style=\"border-width:0\" src=\"https://i.creativecommons.org/l/by-nc/4.0/88x31.png\" /\u003e\u003c/a\u003e\u003cbr /\u003eThis work is licensed under a \u003ca rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc/4.0/\"\u003eCreative Commons Attribution-NonCommercial 4.0 International License\u003c/a\u003e.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fatheobold%2Fdata-science-ws","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fatheobold%2Fdata-science-ws","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fatheobold%2Fdata-science-ws/lists"}