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Data Visualization: About Data Visualization

This LibGuide collects resources and tutorials related to data visualization. It is a companion to the visualization services provided by Data and Visualization Services at Duke University Libraries.

Why Visualize?

There have been many attempts to explain why visualization might be a useful practice.  Some of these explanations are anecdotal, but there are increasingly compelling arguments that support visualization as a useful component of data analysis and research in general.


Data visualization can be a complicated set of processes to learn.  A good starting place is to learn a bit of the vocabulary you may see in the various tools and tutorials.

  • Data visualization:

    an umbrella term, usually covering both information and scientific visualization.  This is a general way of talking about anything that converts data sources into a visual representation (like charts, graphs, maps, sometimes even just tables).

    • Scientific visualization:

      generally, the visualization of scientific data that have close ties to real-world objects with spatial properties.  An example might be visualizations of air flow over the wing of an airplane, or 3D volumes generated from MRI scans.  The goal is often to generate an image of something for which we have spatial information and combine that with data that is perhaps less directly accessible, like temperate or pressure data.  The different scientific fields often have very specific conventions for doing their own types of visualizations.

    • Information visualization:

      also a broad term, covering most statistical charts and graphs but also other visual/spatial metaphors that can be used to represent data sets that don't have inherent spatial components.

    • Infographic:

      a specific sort of genre of visualizations.  Infographics have become popular on the web as a way of combining various statistics and visualizations with a narrative and, sometimes, a polemic.

  • Visual analytics:

    the practice of using visualizations to analyze data.  In some research, visualizations can support more formal statistical tests by allowing researchers to interact with the data points directly without aggregating or summarizing them.  Even simple scatter plots, when the variables are chosen carefully, can show outliers, dense regions, bimodalities, etc. In fields where the data themselves are visual (e.g., medical fields), visual analytics may actually be the primary means of analyzing data.  The process of analyzing data through visualization is itself studied by researchers in the visual analytics field.