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 can be expressed in different forms.
- Charts are a common way of expressing data, as they depict different data varieties and allow data comparison.
- The type of chart you use depends primarily on two things:
- The data you want to communicate
- What you want to convey about that data
- These guidelines provide descriptions of various different types of charts and their use cases

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University of Michigan - Ann Arbor

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

Data Visualization

- Data visualization is a form of communication that portrays dense and complex information in graphical form. 
- The resulting visuals are designed to make it easy to compare data and use it to tell a story 
-  Both of which can help users in decision making

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