The Journal of Statistics Education(JSE) disseminates knowledge for the improvement of statistics education at all levels, including elementary, secondary, post-secondary, post-graduate, continuing, and workplace education. It is distributed electronically and, in accord with its broad focus, publishes articles that enhance the exchange of a diversity of interesting and useful information among educators, practitioners, and researchers around the world. The intended audience includes anyone who teaches statistics, as well as those interested in research on statistical and probabilistic reasoning. All submissions are rigorously refereed using a double-blind peer review process.
Beginning in 2019 the journal will be adding a section on "Teaching Data Science" that will be headed by Nicholas Horton (Amherst College). The 2018 National Academies Report "Data Science for Undergraduates: Opportunities and Options" (https://nas.edu/envisioningds) noted that a critical task in the education of future data scientists and statisticians is to instill data acumen, which was defined in terms of the following areas:
- Mathematical foundations for data science,
- Computational foundations for data science,
- Statistical foundations,
- Data management and curation,
- Data description and visualization,
- Data modeling and assessment,
- Workflow and reproducibility,
- Communication and teamwork,
- Domain-specific considerations, and
- Ethical problem solving.
To make sense of the data that surrounds them, students require exposure to key concepts in data science, a foundation in data technologies, opportunities to undertake the complete data analysis cycle, and an understanding of ethical considerations. While these have always been part of the mission of JSE, these learning outcomes, while part of a traditional statistics curriculum, now are receiving considerably more emphasis.
For this new section we envision a mixture of "what" and "how" articles related to data science and data acumen. What should students learn about computation and data technologies? How should analytic tools and workflows be taught?
We see this new section as a way to (a) help statisticians be aware of what is happening at the intersection of statistics, computer science, and domain application areas and (b) help the community develop greater facility with more sophisticated algorithmic foundations, data technologies, and novel statistical methods. We hope that a regular series of papers in JSE will benefit readers who want to explore how to teach these new learning outcomes now prominently appearing in statistics and data science courses and programs. Other papers in this section might explore new technologies, approaches, techniques, activities, courses, or new units added to existing courses.
More information about the journal can be found at https://www.tandfonline.com/toc/ujse20
Prospective authors are encourage to contact Nicholas Horton (editor of the "Teaching Data Science" section, nhorton@amherst.edu) or Jeffrey Witmer (JSE editor, jeff.witmer@oberlin.edu).
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Nicholas Horton
Beitzel Professor of Technology and Society (Statistics and Data Science)
Amherst College
Amherst, MA United States
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