Schedule

Readings are subject to change. Check the online syllabus before reading. Please refer to the learning management system (LMS) for all assignment deadlines and expected deliverables.

Week 1

• Tue., Jan. 16 Introductions
  • Reading:
    • VAD 1: What is Vis?
• Fri., Jan. 19 Orienting yourself to Web Visualizations
  • Reading:
    • Murray 1-2
    • Introduction to Observable
    • Optional: A Minimal Introduction to JavaScript and Observable. You may want to spend some time here if you are completely new to Javascript or need a refresher on programming in general. If so, review this before the Introduction to Observable above.
    • Optional: Variable assignment in JavaScript: let, const, var for beginners

Week 2

• Tue., Jan. 23 Tech Fundamentals
  • Reading
    • Murray 3-4: Tech Fundamentals & Setup
    • Start Introduction to Vega-Lite
• Fri., Jan. 26 Data
  • VAD 2: What: Data Abstraction
  • Murray 5: Data
  • Data Types - read up to, but not through, the section named “Encoding Channels”

Week 3

• Tue., Jan. 30 Tasks
  • VAD 3: Why: Task Abstraction
  • Continue Data Types, Graphical Marks, and Visual Encoding Channels beginning at “Encoding Channels”
• Fri., Feb. 2 Marks and Channels
  • Reading
    • VAD 5: Marks and Channels
    • Data Types, Graphical Marks, and Visual Encoding Channels - finish.

Week 4

• Tue., Feb. 6 Exploratory Data Analysis
  • Reading
    • Polaris: A System for Query, Analysis and Visualization of Multi-dimensional Relational Databases. Chris Stolte, Diane Tang, and Pat Hanrahan. IEEE TVCG. 2002.
    • Data Transformation
• Fri., Feb. 9 Exploratory Data Analysis II
  • Reading
    • Introduction to Tableau
    • Scales, Axes, and Transformation

Week 5

• Tue., Feb. 13 Tables I
  • Reading
    • VAD 7: Arrange Table Data
• Fri., Feb. 16 Tables II
  • Reading
    • VAD 12: Facet
    • Multi-view Composition

Week 6

• Tue., Feb. 20 Spatial Data I
  • Reading
    • VAD 8: Spatial Data
    • Cartographic Visualization
• Fri., Feb. 23 Spatial Data II
  • Reading
    • Interaction

Week 7

• Tue., Feb. 27 Networks I
  • Reading
    • VAD 9: Network Data
• Fri., Mar. 1 Networks II

Week 8

• Tue., Mar. 5 Introduction to D3 I
  • Reading
    • Introduction to D3 - Static Visualizations
• Fri., Mar. 8 Introduction to D3 II
  • Reading
    • D3 Tutorial Part II

Week 9

• Tue., Mar. 12 Spring break; no classes, university offices open Monday through Friday
• Fri., Mar. 15 Spring break; no classes, university offices open Monday through Friday

Week 10

• Tue., Mar. 19 Color and Perception I
  • Reading
    • VAD 6: Rules of Thumb
• Fri., Mar. 22 Color and Perception II
  • Reading
    • VAD 10: Color and Other Channels

Week 11

• Tue., Mar. 26 Manipulate
  • Reading
    • VAD 11: Manipulate View
• Fri., Mar. 29 Presenting Visual Statistics
  • Reading
    • Tufte VST

Week 12

• Tue., Apr. 2 3D Visualization I
  • Optional: Kraus, M., Fuchs, J., Sommer, B., Klein, K., Engelke, U., Keim, D., & Schreiber, F. (2022, February). Immersive analytics with abstract 3D visualizations: A survey. In Computer Graphics Forum (Vol. 41, No. 1, pp. 201-229).
• Fri., Apr. 5 3D Visualization II
  • Optional: Djavaherpour, H., Samavati, F., Mahdavi‐Amiri, A., Yazdanbakhsh, F., Huron, S., Levy, R., … & Oehlberg, L. (2021, June). Data to physicalization: A survey of the physical rendering process. In Computer Graphics Forum (Vol. 40, No. 3, pp. 569-598).

Week 13

• Tue., Apr. 9 Reduce
  • Reading
    • VAD 13: Reduce Items and Attributes
• Fri., Apr. 12 Embedding, Focus + Context
  • Reading
    • VAD 14: Embed Focus + Context

Week 14

• Tue., Apr. 16 Evaluating Visualizations
  • Reading
    • VAD 4: Evaluation
• Fri., Apr. 19 Project Videos

Week 15

• Tue., Apr. 23 Text
  • Chapter 2 of Cao, N., & Cui, W. (2016). Introduction to text visualization (Vol. 1). Paris: Atlantis Press.
• Fri., Apr. 26 Visual Analytics, Machine Learning
  • La Rosa, B., Blasilli, G., Bourqui, R., Auber, D., Santucci, G., Capobianco, R., … & Angelini, M. (2023, February). State of the art of visual analytics for explainable deep learning. In Computer graphics forum (Vol. 42, No. 1, pp. 319-355).
  • Sperrle, F., El‐Assady, M., Guo, G., Borgo, R., Chau, D. H., Endert, A., & Keim, D. (2021, June). A survey of human‐centered evaluations in human‐centered machine learning. In Computer Graphics Forum (Vol. 40, No. 3, pp. 543-568).

Finals

• Fri., May 3 9:10AM-10:40AM: Final Project Presentations (DMTI 213)