LIBR 514F (3)


Cross-listed with ARST 575H; offered on an irregular basis


ARST 575H: completion of the MAS core courses for MAS students

LIBR 514F; completion of the MLIS core courses for MLIS students|

Dual students need to have met the prerequisites for the section [ARST or LIBR] in which they are registered.

Note: Knowledge of advanced mathematics is not required for this course.

GOAL: This course provides an overview of the fields of Information Visualization and Visual Analytics. The goal of Information Visualization is to use human perceptual capabilities to gain insights into large and abstract data sets that are difficult to extract using standard query languages. The goal of Visual Analytics is to synthesize information and derive insight from massive, dynamic, ambiguous and often conflicting data; detect the unexpected; provide timely defensible and understandable assessments; and communicate assessment effectively for action. Emphasis in this course will be placed on understanding Information Visualization and using a Visual Analytics tool for knowledge exploration.


Upon completion of this course students will be able to:

  • Explain the history and development of the fields of information visualization and visual analytics and appreciate the differences between the two approaches
  • Explain and apply the theories related to the visualization of information
  • Explain different ways information can be visualized and the advantages and limitations of each approach in relation to visualization objectives
  • Explain and apply design principles and factors to be considered when creating information visualizations
  • Analyze, describe, classify, and index information visualizations based on a variety of visual, physical, contextual, and interpretive attributes.
  • Critically evaluate an information visualization
  • Create an information visualization using a specific tool
  • Use an information visualization as a tool to conduct analysis
  • Demonstrate visual literacy skills


  • Introduction to Information Visualization and Visual Analytics:
    • Definitions
    • History and development
    • Frameworks
  • Theories of human visual perception and cognition
  • Understanding the information visualization needs and use
    • Presentation
    • Analysis and decision-making
      • Multivariate analysis
      • Exploring relationships
  • Understanding the data
    • Types of data
      • Ordinal data
      • Categorical and nominal data
      • Relational data
      • Geospatial data
    • Data considerations
      • Size/volume
      • Dimensionality
      • Parameters
      • Structure
      • Range
      • Distribution
      • Dynamics
      • Data Quality
  • Transformation information into visualizations
  • Types of information visualizations
  • Identifying and evaluating information visualization/visual analysis tools
    • Data ingest and parsing functionality
    • Visualization functionality
    • Interactivity functionality and human-computer interaction
  • Case studies in the application of information visualization and visual analytics
    • Bibliographic citation analysis
    • Digital Preservation
    • E-Discovery
    • Performance of Algorithms
    • Financial analysis
    • Social network analysis
  • Critical issues and limitations of Information Visualization and Visual Analytics
  • Information Visualizations as Records
  • The Digital Preservation of Information Visualizations
  • The Future of information visualization and Visual Analytics