Data visualizations use custom styles and shapes to make data easier to understand at a glance, in ways that suit the user’s needs and context.

Charts can benefit from customizing the following:

- Graphical elements
- Typography
- Iconography
- Axes and labels
- Legends and annotations

Style in Data Visualization 


- Change over time
- Category comparison
- Ranking
- Part-to-whole
- Correlation
- Distribution
- Flow
- Relationship

Types of Charts

Data visualization design involves two related choices: selecting a chart type suited to the data and intended message, and styling the visualization so its information is clear and appropriate for its context.

Arizona State University

University of Michigan - Ann Arbor

Google

Data visualization depicts information in graphical form:
- Principles
- Types
- Selecting Charts
- Style

Data Visualization

Effective data visualization relies on core principles to ensure that dense and complex information is communicated clearly. These principles guide the design of visuals to make data comparison easy, tell a compelling story, and assist users in decision-making.

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