Professor Jim Ridgway jim.ridgway@durham.ac.uk
Professor Jim Ridgway jim.ridgway@durham.ac.uk
Dr Rosemary Ridgway r.a.ridgway@durham.ac.uk
Associate Professor
J. Nicholson
M. A. Sorto
Editor
A. White
Editor
L. Guyot
Editor
Data science presents both opportunities and threats to conventional statistics courses. Opportunities include being at the bleeding edge of data analysis, and learning new ways to model phenomena; threats include the challenge of learning new skills and reviewing fundamental assumptions about explanation, prediction and modeling. Powerful data visualisations makes it easier to introduce students to fundamental statistical ideas associated with multivariate data. Data science provides methods to tackle problems that are intractable using analytic methods. Students need to learn how to model complex problems, and to understand the problematic nature of modeling – and they need to consider the practical and ethical implications of their (and others’) work. Here, we offer a stroll into the foothills, along with aphorisms and heuristics for data analysts.
Ridgway, J., Ridgway, R., & Nicholson, J. (2018). Data Science for all: A stroll in the foothills. In M. . A. Sorto, A. White, & L. Guyot (Eds.), Looking back, looking forward : proceedings of the Tenth International Conference on Teaching Statistics (ICOTS10, July, 2018), Kyoto, Japan (1-6)
Presentation Conference Type | Conference Paper (Published) |
---|---|
Conference Name | International Conference on Teaching Statistics |
Acceptance Date | Feb 15, 2018 |
Online Publication Date | Mar 25, 2018 |
Publication Date | Mar 25, 2018 |
Deposit Date | Oct 21, 2019 |
Publicly Available Date | Apr 26, 2020 |
Pages | 1-6 |
Book Title | Looking back, looking forward : proceedings of the Tenth International Conference on Teaching Statistics (ICOTS10, July, 2018), Kyoto, Japan. |
Public URL | https://durham-repository.worktribe.com/output/1141698 |
Publisher URL | https://iase-web.org/Conference_Proceedings.php?p=ICOTS_10_2018 |
Published Conference Proceeding
(273 Kb)
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Looking Back- Looking Forward; Statistics and the Data Science Tsunami
(2019)
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Problematising high-stakes assessment in statistics
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Real-world contexts in statistics components of UK mathematics examinations: aiming forward, walking backwards
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Visualizing complex data and working with visual data representations of multivariate data
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Designing Interactive Displays to Promote Effective use of Evidence
(2015)
Presentation / Conference Contribution
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