CS/DATA 6260 : Visualization

CS/DATA 6260: Visualization

Semester Hours:   3.0
Contact Hours:   3
Coordinator:   Jong Kwan "Jake" Lee
Text:   Visualization Analysis and Design
Author:   T. MUNZER
Year:   2014

SPECIFIC COURSE INFORMATION

Catalog Description

The course discusses the principles, methods, and techniques for effective visual analysis of data. Many aspects of visualization, including techniques for both spatial and non-spatial data, are explored. The course topics include an overview of principles from perception and design, a framework for discussing, critiquing, and analyzing visualization, and visualization techniques and methods for a broad range of data types. Hands-on visualization experience using visualization systems and tools are included. Analytic tasks are also performed on the visualization literature. Prerequisites: Admission to MS in CS program, admission to MS/PhD in DS program, or permission of instructor. Credit cannot be earned for both DATA 6260 and CS 6260.

Course type: ELECTIVE

SPECIFIC COURSE GOALS

  • I am able to explain the basic principles in visualization design.
  • I am able to use visualization methods for both spatial and non-spatial data.
  • I am able to analyze the visualization design choices for different problems.
  • I am able to apply data item and attribute reductions for visualization.
  • I am able to utilize popularly used visualization systems and tools.

LIST OF TOPICS COVERED

  • Introduction to Visualization (~5%)
    • Overview and value of visualization
    • The big picture
  • Data Abstraction and Task Abstraction (~10%)
    • Data types, attribute types, semantics
    • Analyzing tasks abstractly, actions (analyze, produce, search, query)
  • Analysis and Mark & Channels (~10%)
    • Levels of design, validations
    • Expressiveness and effectiveness, effectiveness
  • Rules of Thumb in Visualization (~15%)
    • Justifying 3D, 2D
    • Memory and attention, animation and side-by-side views
    • Resolution, responsiveness
  • Tables, Networks and Trees (~10%)
    • Keys and values, categorical regions
    • Spatial axis orientation, spatial layout density
    • Matrix, link marks, hierarchy marks
  • Spatial Data (~10%)
    • Geographic data, scalar fields, vector fields, tensor fields
  • Map Color and Other Channels (~5%)
    • Color theory, colormaps, channels
  • Views (~10%)
    • Manipulating views (selecting elements, changing viewpoint, reducing attributes)
    • Juxtapose and coordinate views
    • Partition, layers
  • Reduce Items and Attributes (~10%)
    • Filtering, aggregate
  • Visualization Tools/Libraries (~15%)
    • E.g., Tableau, D3, Qt, Python, Processing (sketchbook SW), Volume rendering library, controP5 library, etc.

EXAMPLE PROJECTS

  • Data Exploration and Analysis via Visualization Tool
    • Use a visualization tool to help users visually explore complex data and confirm hypothesis about the data.
    • Formulate and answer a series of specific questions about a specific data, and then, create a final visualization that is designed to communicate the findings about the data
  • Exploring Time Series Data
    • Time series data are used very often these days, e.g., in medicine, finance, history, climatology, etc.
    • Implement/develop an interactive viewer for looking at time series that explores several different visual representations.
  • Exploring Multidimensional Data
    • Multidimensional data exploration is a challenging task in visualization.
    • Implement the Parallel Coordinates (widely used visual representation) with the support of the following interactivities: filtering the data across multiple attributes, reordering the axes, inverting the axes
    • Aggregate multidimensional data into clusters of similar data points
  • Transfer Function Design
    • Implement a volume renderer for 3D volume dataset
    • Design transfer functions for specific volume dataset with your own control panel widget

RECOMMENDED REFERENCES

  • Information Visualization: Perception for Design, 3rd ed., by Colin Ware, Morgan Kaufmann
  • Visual Thinking for Design, 1st ed., by Colin Ware, Morgan Kaufmann
  • Visualizing Data: Exploring and Explaining Data with the Processing Environment, 1st ed., by Ben Fry, O’Reilly Media

Updated: 12/17/2025 05:08PM