[ABE-L] Fwd: Statistical Education Section : "Teaching Data Science" and the Journal of Statistics Education

Doris Fontes dsfontes em gmail.com
Dom Nov 25 09:12:43 -03 2018


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From: Nicholas Horton via American Statistical Association <
Mail em connectedcommunity.org>
Date: sáb, 24 de nov de 2018 18:51
Subject: Statistical Education Section : "Teaching Data Science" and the
Journal of Statistics Education
To: <dsfontes em gmail.com>


The Journal of Statistics Education(JSE) disseminates knowledge for the
improvement of statistics education at all levels, including elementary,...
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Nov 24, 2018 3:52 PM
Nicholas Horton <https://community.amstat.org/people/nicholas-horton>

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" (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
www.tandfonline.com/toc/ujse20

Prospective authors are encourage to contact Nicholas Horton (editor of the
"Teaching Data Science" section, nhorton em amherst.edu) or Jeffrey Witmer
(JSE editor, jeff.witmer em oberlin.edu).


------------------------------
Nicholas Horton
Beitzel Professor of Technology and Society (Statistics and Data Science)
Amherst College
Amherst, MA United States
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