Visualizing Data
Data composition: The process of combining the individual parts in a visualization and displaying them together as a whole.
Types of Data Visualization
Dynamic visualization: Data visualizations that are interactive or change over time. **
Static visualization: A data visualization that does not change over time unless it is edited.
Histogram: A data visualization that shows how often data values fall into certain ranges.
Bar graph: A data visualization that uses size to contrast and compare two or more values.
Distribution graph: A data visualization that displays the frequency of various outcomes in a sample.
Bullet graph: A data visualization that displays data as a horizontal bar chart moving toward a desired value.
Line graph: A data visualization that uses one or more lines to display shifts or changes in data over time.
Scatter plot: A data visualization that represents relationships between different variables with individual data points without a connecting line.
Box plot: A data visualization that displays the distribution of values along an x-axis.
Chart: A graphical representation of data from a worksheet.
Pie chart: A data visualization that uses segments of a circle to represent the proportions of each data category compared to the whole.
Area chart: A data visualization that uses individual data points for a changing variable connected by a continuous line with a filled in area underneath.
Bubble chart: A data visualization that displays individual data points as bubbles, comparing numeric values by their relative size.
Column chart: A data visualization that uses individual data points for a changing variable, represented as vertical columns.
Combo chart: A data visualization that combines more than one visualization type.
Donut chart: A data visualization where segments of a ring represent data values adding up to a whole.
Gantt chart: A data visualization that displays the duration of events or activities on a timeline.
Gauge chart: A data visualization that shows a single result within a progressive range of values.
Packed bubble chart: A data visualization that displays data in clustered circles.
Map: A data visualization that organizes data geographically.
Heat map: A data visualization that uses color contrast to compare categories in a dataset.
Density map: A data visualization that represents concentrations, with color representing the number or frequency of data points in a given area on a map.
Filled map: A data visualization that colors areas in a map based on measurements or dimensions.
Symbol map: A data visualization that displays a marker over a given longitude and latitude.
Decision tree: A tool that helps analysts make decisions about critical features of a visualization.
Circle view: A data visualization that shows comparative strength in data.
Highlight table: A data visualization that uses conditional formatting and color on a table.
Understanding Data Visualization
Headline: Text at the top of a visualization that communicates the data being presented.
Subtitle: Text that supports a headline by adding context and description.
Label: Text in a visualization that identifies a value or describes a scale.
Legend: A tool that identifies the meaning of various elements in a data visualization.
X-axis: The horizontal line of a graph usually placed at the bottom, which is often used to represent time scales and discrete categories.
Y-axis: The vertical line of a graph usually placed to the left, which is often used to represent frequencies and other numerical variables.
Alternative text: Text that provides an alternative to non-text content, such as images and videos.
Annotation: Text that briefly explains data or helps focus the audience on a particular aspect of the data in a visualization.
Design Data Visualization
Design thinking: A process used to solve complex problems in a user-centric way.
The Design Principles
Balance: The design principle of creating aesthetic appeal and clarity in a data visualization by evenly distributing visual elements.
Emphasis: The design principle of arranging visual elements to focus the audience’s attention on important information in a data visualization.
Movement: The design principle of arranging visual elements to guide the audience’s eyes from one part of a data visualization to another.
Pattern: The design principle of using similar visual elements to demonstrate trends and relationships in a data visualization.
Proportion: The design principle of suing the relative size and arrangement of visual elements.
Rhythm: The design principle of creating movement and flow in a data visualization to engage an audience.
Unity: The design principle of using visual elements that complement each other to create.
Variety: The design principle of using different kinds of visual elements in a data visualization to engage an audience.
Visual Forms in Data Visualization
Visual form: The appearance of a data visualization that gives it structure and aesthetic appeal.
Channel: A visual aspect or variable that represents characteristics of the data in a visualization.
Cluster: A collection of data points on a data visualization with similar values.
Mark: A visual object in a data visualization such as a point, line, or shape.
Pre-attentive attributes: The elements of data visualization that an audience recognizes automatically without conscious effort.
Diverging color palette: A color theme that displays two ranges of data values using two different hues, with color intensity representing the magnitude of the values.
Ways for Data Visualization
R: A programming language used for statistical analysis, visualization, and other data analysis.
Tableau: A business intelligence and analytics platform that helps people visualize, understand, and make decisions with data.
Data blending: A tableau method that combines data from multiple data sources.