HD·ID·02 Basic → Advanced
Data Visualization Principles
Turning numbers into honest, readable shapes.
Data visualization translates quantitative information into visual form. This course covers chart selection, the perceptual hierarchy of visual encodings, and the honesty problems, misleading axes, cherry-picked scales, that make otherwise accurate data communicate a false story.
In Development
This course's full module content is being written. The outline below is final, subscribe for updates when it publishes.
LEARNING OUTCOMES
- 01 Select an appropriate chart type for a given dataset and comparison task
- 02 Rank visual encodings (position, length, colour, area) by perceptual accuracy
- 03 Identify common techniques that make a chart misleading despite accurate data
- 04 Design a chart that supports a single, clear takeaway
- 05 Critique a published chart for both encoding choice and honesty
- Match chart type to comparison type, not to taste
- Bar, line, and scatter each answer a different question best
- Position and length are the most accurate encodings
- Colour and area are useful for secondary, not primary, comparisons
- A truncated axis exaggerates apparent change
- Consistent scales are required for fair visual comparison
- Sequential schemes suit ordered data; diverging suit data with a meaningful midpoint
- Categorical colour should never imply an order that isn't there
- Annotation directs attention to the intended takeaway
- Unannotated charts leave interpretation entirely to the viewer
- Audit before publishing, not after criticism
- Accurate data can still be displayed dishonestly