Among other topics, the course covers:

  • Stakeholder needs acquisition & project specification
  • Data mining algorithms and visualization tools
  • Temporal, geospatial, topical, and network visualization techniques
  • Research and development frontiers
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Data Visualization Literacy

In the information age, being able to create and interpret data visualizations is as important as being able to read and write text. This course introduces a theoretical visualization framework to define, measure, and advance student ability in data visualization literacy, discussed in part two in the Atlas of Knowledge, published by The MIT Press. The framework is used to organize course content and exams; support the design of effective workflows; to guide visual design, i.e., the mapping of data variables to graphic valuable types and graphic symbol types; and to effectively communicate using proper terminology.