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.
Sterratt et al. (Thu,) studied this question.