CS 4460, formally known as Introduction to Information Visualization or Infovis for short, is a 3 credit hour Computer Science course about data visualization and interactivity. The course counts as both a Human-Centered Technology elective for the People thread and a Media Technologies elective for the Media thread.
The course surveys a breadth of visualization approaches and interaction methods, and outlines how the research space has evolved over time. Students learn principles of effective visual communication, implement visualizations with D3.js, and analyze and critique the merits and limitations of different visualization approaches.
Topic List
Lecture Topic List
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- Infovis overview
- Multivariate data and charts
- Parallel coordinates
- Attribute explorer
- Scatterplot matrix
- Star plots
- Star coordinates
- Small multiples
- Mosaic plots
- Attribute explorer
- Dust & magnet
- Perception and Gestalt
- Two-stage model of perceptual processing
- Gestalt principles
- Visual chart design guidelines and principles (Tufte and Few)
- Graphical integrity
- Lie factor
- Data-ink ratio
- Tasks and analysis
- Schneiderman's task x datatype taxonomy
- Schneiderman's mantra (overview first, zoom and filter, details on demand)
- Mental models
- Sensemaking loop
- Data frame model
- ICE-T evaluation
- User interaction
- User Interaction taxonomies (Yi et al.)
- Querying and dynamic query
- Query controls
- Text visualization
- Information retrieval
- Visualizing search queries
- Word clouds
- Word trees
- Phrase nets
- Theme/topic analysis
- Graphs and networks
- Schneiderman's Netviz Nirvana
- Graph/Network task taxonomy
- Graph layout approaches
- Scale challenges
- Graph querying
- Hierarchies and trees
- Node-link diagrams
- SpaceTree
- Indented lists
- Hyperbolic browser
- Flextree
- Space-filling representations
- Icicle plot
- Treemap
- Context treemap
- Storytelling
- Communicating insights
- Hans Rosling
- Data-driven storytelling
- Exploration vs explanation
- Visual analytics
- Integration of data mining and machine learning algorithms
- Time-series and temporal visualizations
- Time taxonomy
- Querying time-series data
- Explainability
- Evaluation
- Utility vs usability
- Schneiderman & Plaisant's MILC technique (multi-dimensional, in-depth, long-term case study)
- ICE-T evaluation
- Data humanism
- Geospatial visualization
- Geometry
- Modifiable areal unit problem (MAUP)
- Cartograms
- Scalar fields and isolines
- Post-WIMP visualization; visualization tools and toolkits
- Vis on other devices
- Small-scale (mobile/touch)
- Large-scale (large/multiple displays)
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Lab Topic List (Alex Endert, Fall 2021)
- Lab 1: Intro to HTML, CSS, and SVG
- Lab 2: JavaScript 101
- Lab 3: Intro to D3
- Lab 4: D3 Selections and Grouping
- Lab 5: D3 Enter-Update-Exit Pattern and Filter
- Lab 6: Brushing and Linking
- Lab 7A: Force Directed Graph
- Lab 7B: Brushing and Linking
- Lab 7C: Scrollytelling
- Lab 7D: Interactive Visual Comparison
Class Structure
WIP
Prerequisite Knowledge
A formal requirement of CS 1332 with a C or higher is required to take this class. This class is only offered to students with junior or senior status.
Although no prior knowledge of HTML, CSS, and JavaScript is necessary, some familiarity with HTML, CSS, and JavaScript may help during labs.
Scheduling
WIP
Resources
WIP
