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Data Visualization: About Data Visualization

This Libguide has been adapted from Duke University (Data Visualization: About Data Visualization @ https://guides.library.duke.edu/datavis), guide created by Angela Zoss. Modified by Jose-Ignacio Pareja

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.

Glossary

Learning the Data visualization vocabulary would be a good starting place to learn about DataViz.

  • Data visualization: is an umbrella term, usually covering both information and scientific visualization.  In other words we are 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.
       
    • Infographica 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 analyticsthe 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.

References

This Libguide has been adopted from Duke University (Data Visualization: About Data Visualization @ https://guides.library.duke.edu/datavis), guide created by Angela Zoss

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