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
Register for the Course

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.

Teamwork

Students will collaborate in small teams on on projects that use real world data. Teams select from a variety of client projects curated by course instructors The current list of clients and projects is available here. Results from previous client projects were published in the Visual Insights textbook by The MIT Press.

Learn About Group Projects →
Team Collaboration on Real World Project

Real World Projects

students from around the world

Students from Around the World

IVMOOC students come from more than 100 countries; have expertise in diverse areas of science and technology; work in academia, industry, and government; from high school degrees to PhDs. Learn in truly international and diverse team and collaborate on projects that make a positive difference in the lives of many people.

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.