Data Visualization Basics

The process of creating visual representations of data to communicate insights and facilitate understanding.

Decision makers often struggle to grasp complex data analysis. DV makes it easier to understand and interpret information.

The steps involved in creating a data visualization, including acquiring data, parsing it, filtering relevant information, mining for patterns, representing it visually, and allowing interaction with the visualization.

Principles that guide effective data visualization design, aiming to make information clear, concise, and impactful.

Pre-attentive attributes are visual features that are quickly noticed by the human eye, used to attract attention and guide viewers to key information.

Edward Tufte's guidelines emphasize clarity, honesty, and efficiency in data visualization, promoting data-driven insights without misleading or confusing the audience.

Gestalt principles utilize the natural tendency of the human brain to perceive patterns and wholes, helping create harmonious, understandable, and engaging visualizations.

A type of data visualization that uses charts, graphs, and other visual elements to represent data in a clear and concise manner, usually focusing on a single insight or story.

Arranging elements in close proximity to suggest a connection, and separating them from unrelated elements with space.

Creating a clear distinction between the main focus of a visual and the surrounding elements.

Using symmetrical arrangement to convey stability, order, and visual appeal.

Elements moving in the same direction are perceived as related and likely to have a common goal.

Utilizing simplicity and clarity to enhance visual understanding and focus.

Leveraging past experiences to help viewers relate to and interpret visual information.

The core message or central idea conveyed through a visual.

A structured narrative that guides viewers through a visual story.

Pre-attentive attributes are visual features that capture attention quickly and effortlessly, without conscious effort. They include color, form, movement, and spatial positioning.

Tufte's Principle of Graphical Integrity emphasizes the truthfulness of visual representations of data. It ensures that the visual presentation accurately reflects the underlying data and avoids misleading interpretations.

Tufte's Principle of Maximizing Data Ink encourages designers to use the minimum amount of ink necessary to represent data effectively. It emphasizes maximizing the ratio of data ink to non-data ink, eliminating unnecessary elements that distract from the information.

Tufte's Principle of Reducing Chart Junk advocates for minimizing visual clutter and unnecessary elements in data visualizations. It promotes clarity and readability by removing distracting features that don't contribute to understanding the data.

Tufte's Principle of High Data Density encourages a compact and informative layout in visualizations. It aims to maximize the amount of data represented within a given area, making the visualization more concise and efficient.

Tufte's Principle of Small Multiples suggests using multiple small visualizations to show variations or trends across different groups or time periods. It provides a comprehensive and comparative view of the data by presenting multiple perspectives in a concise format.

The Lie Factor is a metric used to assess the accuracy of visual representations of data. It quantifies the difference between the visual size or magnitude of an effect in a visualization and its actual size in the underlying data.

Gestalt Principles, in the context of visual design, refer to a set of principles that describe how humans perceive visual elements and organize them into meaningful patterns and structures.

A layout where users can navigate through different sections of the dashboard to explore specific data.

Options that allow users to filter and customize the data displayed on the dashboard, such as by time period or location.

Using too many colors can make a dashboard confusing and distracting. It should be used strategically to emphasize specific data.

When a dashboard uses the same color for different types of data. This can lead to misinterpretations and make it harder to understand the relationships between different data points.

3D effects on charts can make them visually appealing but also harder to read accurately.