Claudio Silva

Claudio Silva

Institute Professor, New York University

CS-GY 6313 B: Information Visualization - Fall 2026 Detailed Schedule

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⚠️ Work in progress: This schedule is tentative and is being actively updated ahead of and during the semester. Topics, readings, lab sessions, and due dates may change. Check back regularly and watch course announcements and Discord.

Detailed Schedule

Week 1 (Sept 4) - 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:

Optional Reading:

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 10)

Week 2 (Sept 11) - Analytical Questions and Data Transformation

Learning Objectives: Transform questions into visual queries; understand data transformation pipelines

Lecture: Week 2 - Analytical Questions and Data Transformation

  • 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:

Optional Reading:

Lab: Week 2 Lab - Intro to Vega-Lite Data Transformations, Working with Real Datasets

  • 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 17)

Week 3 (Sept 18) - 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

Materials:

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:

Lab: Lab: Fundamental Graphs and Visual Encoding

  • 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 24)

Week 4 (Sept 25) - 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:

Optional Reading:

Lab: Lab: First D3 programming session, DOM manipulation, Data binding

  • First D3 programming session
  • DOM manipulation exercises
  • Data binding concepts
  • Create simple bar chart in D3

Assignment: Exercise 4 - Perception-based design decisions + D3 implementation (due Oct 1)

Week 5 (Oct 2) - 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:

Optional Readings:

Lab: Color scale exercises, Choropleth maps, Accessibility testing

  • D3 scales implementation
  • Color scheme creation and testing
  • Accessibility testing tools
  • Apply color theory to previous D3 examples

Assignment: Exercise 5 - Color design with D3 scales (due Oct 8)

Week 6 (Oct 9) - Group Projects and Design Ethics

Learning Objectives: Understand group project requirements and milestones; identify misleading visualizations; recognize ethical design principles

Lecture: Week 6 - Group Projects

  • Group project overview and timeline
  • Five milestones: Proposal, Data & Sketches, First Draft, Second Draft, Final
  • Team formation and collaboration strategies
  • Choosing topics and datasets (focus on NYC urban data)
  • Example projects and evaluation criteria
  • Plus: Deceptive visualization and design ethics discussion

Required Readings:

Optional Readings:

Lab: Lab: Intro to Interactions and Deceptive Visualizations

  • Team formation activities
  • NYC Open Data exploration
  • Project brainstorming and proposal planning
  • Start forming teams on Discord #project-teams

Assignment:

  • Form teams by Oct 16
  • Browse NYC Open Data for project ideas
  • Exercise 6 - Design misleading vs. honest versions of same data (due Oct 15)

Note: Fall Break falls on Monday, October 12, and does not affect Friday sessions.

Week 7 (Oct 16) - Interaction in Visualization

Learning Objectives: Understand why interaction is essential for data exploration; master the 12 interactive dynamics; design effective interactive visualizations

Lecture: Week 7 - Interactivity in Information Visualization

  • Why interaction matters: From presentation to exploration
  • Bridging the gulfs (HCI concepts)
  • Shneiderman’s Visual Information Seeking Mantra
  • The 12 interactive dynamics (Heer & Shneiderman taxonomy):
    • Data & View Specification: Visualize, Filter, Sort, Derive
    • View Manipulation: Select, Navigate, Coordinate, Organize
    • Process & Provenance: Record, Annotate, Share, Guide
  • Modern interaction frameworks (Libra)
  • Case studies: FilmFinder, VisTrails, TaxiVis

Required Readings:

Recommended Readings:

Lab: Lab: Building Interactive Visualizations

  • D3 event handling (hover, click, brush)
  • Implementing filtering and dynamic queries
  • Tooltip and details-on-demand
  • Brushing and linking across multiple views
  • Creating coordinated visualizations

Assignment: Exercise 7 - Interactive visualization design and implementation (due Oct 22)

Week 8 (Oct 23) - 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:

Optional Readings:

Lab: Plotting and Troubleshooting GeoJSON with D3

  • 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 5)

Week 9 (Oct 30) - Urban Visualization I: Flows, Time & Interactivity

Learning Objectives: Design visual query models for large spatio-temporal urban data; understand brushing and linking at scale

Lecture: Week 9 - Urban Visualization I

  • Urban data characteristics (scale, density, complexity, dynamism)
  • Visual query models for spatio-temporal data
  • TaxiVis case study: temporal and spatial queries, origin-destination flows
  • Brushing and linking across coordinated views
  • Performance optimization (k-d trees, level-of-detail rendering)
  • Case studies: social inequality, transportation hubs, Hurricane Sandy

Required Readings:

Optional Readings:

Lab: Building interactive spatio-temporal visualization

  • D3 time scales and axes
  • Building coordinated linked views
  • Brushing and zooming over spatio-temporal data
  • Origin-destination flow rendering

Assignment: Mini-project 2 begins - Temporal visualization (due Nov 19)

Week 10 (Nov 6) - Urban Visualization II: 3D Form, Design & Simulation

Learning Objectives: Link 3D city models with 2D data views; reason critically about when 3D is warranted

Lecture: Week 10 - Urban Visualization II

  • Kevin Lynch’s “Image of the City”
  • Challenges of 3D urban planning visualization
  • The Urbane framework: linking 3D city models with 2D data views
  • Interactive impact analysis (sky exposure, shadows, viewsheds)
  • Performance-driven design: exploring thousands of building variants
  • Critical reflection on when to use 3D vs 2D

Required Readings:

Optional Readings:

  • CesiumJS - 3D geospatial visualization
  • Deck.gl - GPU-powered visualization layers

Lab: 3D Visualization: Transformations

  • Common Libraries for 3D Visualizations
  • Re-Visiting Algebra
  • 2D Transformations
  • Homogeneous Coordinates
  • 3D Transformations
  • Model-View-Projection

Assignment: Continue Mini-project 2 (due Nov 19)

Week 11 (Nov 13) - Visualizing Time-Oriented Data

Learning Objectives: Design effective time series visualizations; understand sequential, cyclic, and hierarchical time structures

Lecture: Week 11 - Visualizing Time-Oriented Data

  • Temporal data fundamentals (event vs measurement data)
  • Time structures: sequential, cyclic, hierarchical
  • Line charts and aspect ratios (Banking to 45°)
  • Multiple time series: spaghetti plots vs small multiples
  • Stacked area charts and their limitations
  • Semantic vs geometric zoom
  • Heat maps and calendar visualizations
  • Periodic patterns: radial layouts and spirals
  • Horizon charts and sparklines

Required Readings:

Optional Readings:

Lab: Building temporal visualizations

  • D3 time scales and axes
  • Line charts and area charts for time series
  • Small multiples for temporal comparison
  • Heat map and calendar layouts

Assignment: Mini-project 3 begins - Network visualization (due Dec 10)

Week 12 (Nov 20) - Clustering and Dimensionality Reduction

Learning Objectives: Understand clustering visualization techniques; master PCA, t-SNE, and UMAP for high-dimensional data visualization; recognize critical pitfalls in dimensionality reduction

Lecture: Week 12 - Clustering and Dimensionality Reduction

  • Clustering visualization (K-means, DBSCAN, hierarchical methods)
  • Dimensionality reduction fundamentals
  • Principal Component Analysis (PCA)
  • t-SNE: power and pitfalls
  • UMAP: modern alternative
  • Critical visualization principles for non-linear methods

Required Readings:

Optional Readings:

Lab:

  • Implementing PCA visualizations
  • Exploring t-SNE with multiple perplexity values
  • UMAP parameter tuning
  • Comparing dimensionality reduction methods
  • Avoiding common visualization pitfalls

Assignment: Continue Mini-project 3 + dimensionality reduction exercises (due Dec 3)

Thanksgiving Recess (Nov 26-27) - NO CLASS

Assignment: Continue Mini-project 3 and prepare group project presentations

Week 13 (Dec 4) - Visualizing Network Data

Learning Objectives: Master network and tree visualization techniques; understand layout algorithms; design effective hierarchical visualizations

Lecture: Week 13 - Visualizing Network Data

  • Network data structures and properties
  • Node-link diagrams and force-directed layouts
  • Fixed layout patterns (circular, linear, grid)
  • Matrix representations and adjacency views
  • Tree visualization techniques:
    • Dendrograms and hierarchical clustering
    • Treemaps and space-filling layouts
    • Sunburst and icicle plots
    • Decision tree visualization
  • Edge bundling for hierarchical data
  • Interactive network exploration

Required Readings:

Optional Readings:

Lab: In-class network visualization exercises

  • D3 force simulation implementation
  • Creating node-link diagrams with interactive layouts
  • Matrix view creation and comparison
  • Treemap implementation
  • Network layout algorithm exploration

Assignment: Complete Mini-project 3 (due Dec 10) and prepare final project presentations

Week 14 (Dec 11) - Final Project Presentations and Course Wrap-up

Learning Objectives: Present and critique visualization projects; reflect on learning; plan continued development

Presentations:

  • Group project presentations (Milestone 5)
  • Peer feedback and evaluation
  • Q&A and critique sessions

Wrap-up:

  • Course reflection and key takeaways
  • Resources for continued learning
  • Career advice and next steps
  • Course evaluations