CS-GY 6313 Information Visualization Fall 2025 - Detailed Schedule
CS-GY 6313 Information Visualization Fall 2025 - Detailed Schedule
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Detailed Schedule
Week 1 (Sept 5) - Introduction and Evaluation
Learning Objectives: Understand what visualization is, when to use it, and how to evaluate effectiveness
Lecture: Week 1 - Course Introduction & Syllabus
- Course overview and expectations
- What is information visualization?
- Visualization taxonomy and design space
- Evaluation frameworks and criteria
- Introduction to tools landscape (Vega-Lite, D3, Tableau, etc.)
Required Readings:
- Chapter 1: Information Visualization, in Readings in Information Visualization. Card, Mackinlay, and Shneiderman. 1999.
- Introduction to Vega-Lite (Observable notebook)
Optional Reading:
- Decision to Launch the Challenger, in Visual Explanations. Edward Tufte.
Lab: Week 1 Lab - Introduction to Observable and Vega-Lite
- Setup Observable accounts
- Create first basic charts in Vega-Lite
- Explore provided datasets
- Chart gallery exploration
Assignment: Exercise 1 - Visualization critique and basic Vega-Lite charts (due Sept 11)
Week 2 (Sept 12) - Analytical Questions and Data Transformation
Learning Objectives: Transform questions into visual queries; understand data transformation pipelines
Lecture:
- From questions to visual mappings
- Data types and structures
- Data transformation operations (filter, aggregate, derive)
- Query-based visualization systems
- Introduction to Observable notebooks
Required Readings:
- Data Types, Graphical Marks, and Visual Encoding Channels (Observable)
- Polaris: A System for Query, Analysis and Visualization of Multi-dimensional Relational Databases. Stolte, Tang, and Hanrahan. IEEE TVCG 2002.
Optional Reading:
- The Eyes Have It: A Task by Data Type Taxonomy, Shneiderman. 1996.
Lab:
- Vega-Lite data transformations
- Working with real datasets in Observable
- Data aggregation and filtering
- Creating derived fields
Assignment: Exercise 2 - Data questions and transformations using Vega-Lite (due Sept 18)
Week 3 (Sept 19) - Fundamental Graphs and Visual Encoding
Learning Objectives: Master basic chart types and understand when to use each; apply grammar of graphics
Lecture:
- Chart types and their purposes
- Marks and channels theory
- Grammar of graphics principles
- Comparison strategies
- When to use different chart types
Required Readings:
- Chapter 1: Graphical Excellence, in The Visual Display of Quantitative Information. Tufte.
- Chapter 2: Graphical Integrity, in The Visual Display of Quantitative Information. Tufte.
- Multi-View Composition (Observable)
Optional Reading:
- Vega-Lite: A Grammar of Interactive Graphics. Wongsuphasawat et al. OpenVis Conf 2017.
Lab:
- Creating multiple chart types in Vega-Lite
- Exploring encoding alternatives for same data
- Small multiples and faceting
- Combining multiple views
Assignment: Exercise 3 - Chart design and encoding alternatives (due Sept 25)
Week 4 (Sept 26) - Deceptive Visualization and Design Ethics
Learning Objectives: Identify misleading visualizations; understand ethical design principles; recognize cognitive biases
Lecture:
- Types of misleading visualizations
- Intentional vs. unintentional deception
- Cognitive biases in visualization interpretation
- Ethical responsibilities of designers
- Case studies of deceptive visualizations in media
Required Readings:
- Chapter 2: Graphical Integrity, in The Visual Display of Quantitative Information. Edward Tufte. 2001.
- Misinformed by Visualization: What Do We Learn From Misinformative Visualizations?. Lo, Gupta & Shigyo. EuroVis 2022.
Optional Readings:
- Truncating the Y-Axis: Threat or Menace?. Correll, Bertini & Franconeri. 2020.
- Viral Visualizations: How Coronavirus Skeptics Use Orthodox Data Practices to Promote Unorthodox Science Online. Lee et al. ACM CHI 2021.
Lab:
- Analyze deceptive visualization examples
- Create misleading and honest versions of same data
- Redesign problematic visualizations
- Discussion and critique session
Assignment: Exercise 4 - Design misleading vs. honest versions of same data (due Oct 2)
Week 5 (Oct 3) - Visual Perception and D3 Foundations
Learning Objectives: Understand human visual perception principles; begin D3 programming
Lecture:
- Pre-attentive processing and visual attention
- Gestalt principles in visualization
- Color perception and accessibility
- Introduction to D3.js: concepts and architecture
- DOM manipulation basics
Required Readings:
- The Science of Visual Data Communication: What Works. Franconeri et al. Psychological Science in the Public Interest. 2021.
- 39 Studies About Human Perception in 30 Minutes. Kennedy Elliott.
Optional Reading:
- Graphical Perception: Theory, Experimentation and Application. Cleveland & McGill. 1984.
Lab:
- First D3 programming session
- DOM manipulation exercises
- Data binding concepts
- Create simple bar chart in D3
Assignment: Exercise 5 - Perception-based design decisions + D3 implementation (due Oct 9)
Week 6 (Oct 10) - Color and D3 Scales
Learning Objectives: Master color theory for visualization; implement D3 scales and color schemes
Lecture:
- Color theory fundamentals
- Perceptual color spaces (RGB, HSL, LAB)
- Colorblindness and accessibility
- Color palette design strategies
- D3 scales: linear, ordinal, time, color
Required Readings:
- Which color scale to use when visualizing data. Lisa Charlotte Rost.
- Modeling Color Difference for Visualization Design. Danielle Szafir. IEEE TVCG, 2017.
Optional Readings:
- Somewhere Over the Rainbow: An Empirical Assessment of Quantitative Colormaps. Liu & Heer. ACM CHI 2018.
- Color Use Guidelines for Mapping and Visualization. Cynthia Brewer. 1994.
Lab:
- D3 scales implementation
- Color scheme creation and testing
- Accessibility testing tools
- Apply color theory to previous D3 examples
Assignment: Exercise 6 - Color design with D3 scales (due Oct 16)
Fall Break (Oct 11-13) - NO CLASS
Week 7 (Oct 17) - Interaction and Animation
Learning Objectives: Design effective interactions; implement smooth animations and transitions
Lecture:
- Interaction design principles
- Types of interaction (selection, filtering, details-on-demand)
- Animation theory and when to use it
- Easing functions and timing
- Coordinated multiple views
Required Readings:
- Interactive Dynamics for Visual Analysis. Heer & Shneiderman. 2012.
- Effectiveness of Animation in Trend Visualization. Robertson et al. InfoVis 2008.
- Easing Functions Cheat Sheet
Optional Readings:
- What is Interaction for Data Visualization? Dimara & Perin. IEEE TVCG, 2019.
- Animated Transitions in Statistical Data Graphics. Heer & Robertson. IEEE InfoVis 2007.
Lab:
- D3 event handling (hover, click, brush)
- Creating smooth transitions and animations
- Implementing tooltip and details-on-demand
- Coordinating multiple views
Assignment: Exercise 7 - Interactive visualization with smooth transitions (due Oct 23)
Week 8 (Oct 24) - Geographic and Urban Visualization I
Learning Objectives: Understand map projections and geographic data; create effective choropleth and point maps
Lecture:
- Map projections and their trade-offs
- Geographic data formats (GeoJSON, TopoJSON, Shapefiles)
- Choropleth map design principles
- Point mapping and density visualization
- Multi-scale geographic visualization
Required Readings:
- GeoLinter: A Linting Framework for Choropleth Maps. Lei, Fan, MacEachren & Maciejewski. IEEE TVCG 2023.
- Research Challenges in Geovisualization. MacEachren & Kraak. Cartography and Geographic Information Science 2001.
Optional Readings:
- When Maps Shouldn’t Be Maps. Matthew Ericson. 2011.
- Surprise! Bayesian Weighting for De-Biasing Thematic Maps. Correll & Heer. IEEE InfoVis 2017.
Lab:
- D3 geo projection setup
- Loading and displaying maps
- Creating choropleth maps with real data
- Point mapping exercises
Assignment: Mini-project 1 begins - Geographic visualization (due Nov 6)
Week 9 (Oct 31) - Temporal Data and Urban Dynamics
Learning Objectives: Design effective time series visualizations; understand temporal patterns in urban data
Lecture:
- Time series design principles
- Temporal data types and structures
- Animation vs. static temporal representation
- Seasonal and cyclical patterns
- Urban temporal dynamics
Required Readings:
- Visual Exploration of Big Spatio-Temporal Urban Data: A Study of New York City Taxi Trips. Ferreira, Poco, Vo, Freire & Silva. IEEE TVCG 2013.
- Urbane: A 3D Framework to Support Data Driven Decision Making in Urban Development. Ferreira et al. IEEE VIS 2015.
Optional Readings:
- Graphical Perception of Multiple Time Series. Heer, Kong & Agrawala. IEEE InfoVis 2009.
- The Connected Scatterplot for Presenting Paired Time Series. Haroz, Kosara & Franconeri. IEEE TVCG 2016.
Lab:
- D3 time scales and axes
- Line charts and area charts for time series
- Brushing and zooming for temporal data
- Small multiples for temporal comparison
Assignment: Mini-project 2 begins - Temporal visualization (due Nov 20)
Week 10 (Nov 7) - Uncertainty and Data Quality
Learning Objectives: Represent uncertainty visually; assess and communicate data quality issues
Lecture:
- Types of uncertainty in data
- Visual encoding of uncertainty
- Error bars and confidence intervals
- Alternative uncertainty representations
- Data quality assessment and communication
Required Readings:
- The Visual Uncertainty Experience. Jessica Hullman. OpenVis Conf 2016.
- Error Bars Considered Harmful: Exploring Alternate Encodings for Mean and Error. Correll & Gleicher. IEEE InfoVis 2014.
Optional Reading:
- When(ish) is My Bus? User-centered Visualizations of Uncertainty. Kay et al. ACM CHI 2016.
Lab:
- Implementing uncertainty visualizations in D3
- Confidence intervals and error representations
- Alternative uncertainty encodings
- User testing uncertainty representations
Assignment: Continue Mini-project 2 + Exercise 8 - Uncertainty visualization (due Nov 13)
Week 11 (Nov 14) - Network Data and Urban Systems
Learning Objectives: Understand network visualization techniques; apply to urban infrastructure and social systems
Lecture:
- Network data structures and properties
- Node-link diagrams and layout algorithms
- Matrix representations of networks
- Hierarchical and multilevel networks
- Urban networks (transportation, social, infrastructure)
Required Readings:
- Hierarchical Edge Bundles: Visualization of Adjacency Relations in Hierarchical Data. Danny Holten. InfoVis 2006.
- Squarified Treemaps. Bruls, Huizing & van Wijk. 2000.
Optional Reading:
- ManyNets: An Interface for Multiple Network Analysis and Visualization. Freire et al. ACM CHI 2010.
Lab:
- D3 force simulation setup
- Creating node-link diagrams
- Network layout algorithms
- Interactive network exploration
Assignment: Mini-project 3 begins - Network visualization (due Dec 4)
Week 12 (Nov 21) - Scalability and Performance
Learning Objectives: Handle large datasets; optimize visualization performance; understand progressive loading
Lecture:
- Challenges of large dataset visualization
- Data aggregation and sampling strategies
- Progressive loading and level-of-detail
- Performance optimization techniques
- Introduction to WebGL for visualization
Required Readings:
- A Structured Review of Data Management Technology for Interactive Visualization. Battle & Scheidegger. IEEE TVCG. 2020.
- Falcon: Balancing Interactive Latency and Resolution Sensitivity. Moritz, Howe & Heer. ACM CHI 2019.
Optional Readings:
- imMens: Real-time Visual Querying of Big Data. Liu, Jiang & Heer. EuroVis 2013.
- Trust, but Verify: Optimistic Visualizations of Approximate Queries. Moritz et al. ACM CHI 2017.
Lab:
- Data aggregation techniques
- Performance profiling and optimization
- Canvas vs SVG performance comparison
- Introduction to visualization with large datasets
Assignment: Continue Mini-project 3 + optimization exercises (due Nov 25)
Make-up Class (Nov 26 - Wednesday) - Geographic and Urban Visualization II
Learning Objectives: Advanced geographic techniques; intensive project development time
Extended Workshop (2.5 hours with breaks):
- Advanced D3 geo techniques
- Multi-scale mapping strategies
- Spatial analysis and visualization
- Working with urban datasets
- Intensive hands-on work on Mini-project 1
- Individual guidance and troubleshooting
Assignment: Complete any outstanding work on Mini-project 1
Week 13 (Nov 28) - NO CLASS (Thanksgiving)
Assignment: Complete Mini-project 3 and prepare group project presentations
Week 14 (Dec 5) - Advanced Topics and Project Presentations I
Learning Objectives: Explore emerging trends; present and critique visualization projects
Lecture (45 min):
- Emerging trends in visualization
- AI and machine learning visualization
- Virtual and augmented reality applications
- Future directions and career paths
Presentations:
- Group project presentations (first half of teams)
- Peer feedback and discussion
- Q&A and critique sessions
Assignment: Prepare final presentations and project documentation
Week 15 (Dec 12) - Project Presentations II and Course Wrap-up
Learning Objectives: Complete project presentations; reflect on learning; plan continued development
Presentations:
- Final group project presentations (second half)
- Peer feedback and evaluation
- Class discussion of projects and techniques
Wrap-up:
- Course reflection and key takeaways
- Resources for continued learning
- Career advice and next steps
- Course evaluations