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BEDE Network Data Science Skills Curriculum Map

Author(s): Kelly L O'Donnell1, Matthew Aiello-Lammens2, Ellen Bledsoe3, Forrest Joseph Bowlick4, Laura Broughton5, Olga Calderon6, Erika Crispo2, Nate Emery7, Kait Farrell8, Maurice Ngiramahoro9, Nirav Patel10, Shishir Paudel11, Lea Richardson12, Bruno Eleres Soares13, Sarah Supp14, Emily Weigel15

1. Macaulay Honors College 2. Pace University 3. University of Arizona 4. University of Massachusetts - Amherst 5. Bronx Community College/CUNY 6. LaGuardia Community College, CUNY 7. University of California Santa Barbara 8. University of Georgia 9. The Dian Fossey Gorilla Fund 10. Binghamton University 11. Phipps Conservatory and Botanical Gardens 12. California State University Northridge 13. University of Toronto-Scarborough 14. Denison University 15. Georgia Institute of Technology

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Summary:
The Biological and Environmental Data Education (BEDE) Network has produced this data science skills curriculum map as a flexible tool to help instructors highlight data science skills that they are already teaching and to discover new areas to add…

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The Biological and Environmental Data Education (BEDE) Network has produced this data science skills curriculum map as a flexible tool to help instructors highlight data science skills that they are already teaching and to discover new areas to add data science skills that complement the content of their courses or programs. The Curriculum Map is organized around seven major data science skill categories: Data Management, Analysis, Visualization, Coding, Modeling, Ethics, and Reproducibility. Each major category is broken up into subcategories and then skill areas that are associated with specific student learning outcomes. For each skill area, the foundational learning outcomes are indicated.

Licensed under CC Attribution-NonCommercial-ShareAlike 4.0 International according to these terms

Version 1.0 - published on 28 Jun 2024 doi:10.25334/MSFG-6X39 - cite this

Keywords

  1. quantitative skills
  2. scientific literacy
  3. Curriculum development
  4. data science skills training
  5. data literacy skills

Alignments

  1. Resource Type
  2. Audience Level
  3. Activity Length
  4. Open Science and Education Practices Ontology
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