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Actionable Visualization Principles and Guidance for a Foundational University Data Science Course

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Title: Actionable Visualization Principles and Guidance for a Foundational University Data Science Course
Language: English
Authors: David C. Sterratt, Narjes Rohani, Kobi Gal
Source: Teaching Statistics: An International Journal for Teachers. 2026 48(1):S122-S135.
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 14
Publication Date: 2026
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Visualization, Statistics Education, Data Science, Teaching Methods, Undergraduate Study, Undergraduate Students, Introductory Courses, Programming Languages, Graphs
DOI: 10.1002/test.70037
ISSN: 0141-982X
1467-9639
Abstract: When teaching how to describe and apply good practices for visualizing data, we need to define "good". Several sets of guidelines about good visualization practice exist in the literature and online, though each set focuses on different aspects of visualization and their level ranges from very general to very specific. We present five principles and associated guidance that is: (i) appropriate for an entry-level undergraduate data science course where students produce static visualizations using Python or R plotting libraries, (ii) actionable, meaning students and markers can assess visualizations against the guidance, and (iii) concise enough to fit on one page, provided as a resource. We describe how the resource helps our teaching and assessment, and the advice we give students to address the common problem of plots with inaccessibly small text. Informally, student responses to the principles are positive and are continuing to inform changes to the detailed guidance.
Abstractor: As Provided
Notes: https://github.com/Inf2-FDS/fds-visualisation
Entry Date: 2026
Accession Number: EJ1505717
Database: ERIC
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  Data: <searchLink fieldCode="SO" term="%22Teaching+Statistics%3A+An+International+Journal+for+Teachers%22"><i>Teaching Statistics: An International Journal for Teachers</i></searchLink>. 2026 48(1):S122-S135.
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  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
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  Data: When teaching how to describe and apply good practices for visualizing data, we need to define "good". Several sets of guidelines about good visualization practice exist in the literature and online, though each set focuses on different aspects of visualization and their level ranges from very general to very specific. We present five principles and associated guidance that is: (i) appropriate for an entry-level undergraduate data science course where students produce static visualizations using Python or R plotting libraries, (ii) actionable, meaning students and markers can assess visualizations against the guidance, and (iii) concise enough to fit on one page, provided as a resource. We describe how the resource helps our teaching and assessment, and the advice we give students to address the common problem of plots with inaccessibly small text. Informally, student responses to the principles are positive and are continuing to inform changes to the detailed guidance.
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  Data: https://github.com/Inf2-FDS/fds-visualisation
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      – TitleFull: Actionable Visualization Principles and Guidance for a Foundational University Data Science Course
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