MAS.S70 | Applied Data Visualization
Meaning in data is only revealed to those who can bring it to life. With the continual rise of data availability and adoption, the need for sound training in visualization is increasingly important.
Applied Data Visualization is a course designed to equip students with the theoretical and practical tools needed to build effective and engaging data visualizations. By the end of the semester, we aim for students to feel comfortable designing and developing visual stories with data.
The first half of the course consists of instructional learning through workshops and readings. An applied midterm project will help integrate these learnings. The second half of the course focuses on creative learning through working on the final course deliverable: a publication- or release-quality visualization project on a topic of student’s choosing.
Applied Data Visualization is a pass/fail course. Students are expected to critically discuss weekly readings, give and receive peer feedback, and learn while doing. No experience with visualization or programming is necessary. Experience with front-end web development (HTML/CSS/JS), visualization and interface design, and software development processes would help.
|Discussing course plans and expectations
Giving a taste of data visualization through presentations and projects
Learning Day 1
Applied: Hands on with D3Plus
Learning Day 2
Applied: Building custom visualizations with D3
Learning Day 3
|Theory: Visualization Techniques
Applied: Functional front-ends with React
Learning Day 4
|Theory: Multidimensional Visualizations, Maps, and Networks
Applied: Building a complete platform with React & Replot
|Oct 20||Media Lab Fall Member’s Event (No Class)|
|Presentations: Guided Project Demos|
Project Day 1
|Theory: Narrative Visualizations & Intro to Creative Projects
Applied: Starting Work on Course Projects: Project Proposals
|Nov 10||Veteran Day (No Class)|
Project Day 2
|Applied: Project Progress Feedback & Office Hours|
Project Day 3
|Thanksgiving (No Class)|
Final Presentations Day 1
|Theory: Visualization and Collaboration
Applied: Project Presentations Day 1
Final Presentations Day 2
|Theory: Visualization Evaluation
Applied: Project Presentations Day 2 and Wrap Up. Course reflections.
César A. Hidalgo leads the Collective Learning group (formerly Macro Connections) and is an associate professor in the program in media arts and sciences at MIT. Hidalgo’s research focuses on collective learning–the learning taking place in teams, organizations, and economies. With his group, he develops analytical tools and models to understanding how collective learning takes place, and also, they design tools to help improve the collective learning of organizations.
Teaching Staff Contact
Required readings for each week are marked with a [*].
Sep 22: Foundations
- [*] Tufte, The Visual Display of Quantitative Information (Chapters 1-3)
- [*] Anscombe, “Graphs in Statistical Analysis”
- [*] Stevens, “On the Theory of Scales of Measurement” OR Level of measurement
- Bertin, The Semiology of Graphics (Part 1, Chapters 1-2)
Sep 29: Perception
- [*] Ware, Information Visualization: Perception for Design (Chapters 1 and 2)
- [*] Healey, Perception in Visualization
- Rogowitz and Treinish, How NOT to Lie with Visualization
- Elliott, 39 Studies about Human Perception in 30 Minutes
- Healey, Choosing effective colours for data visualization
Oct 06: Visualization Techniques
- [*] Heer, Bostock, and Ogievetsky, A Tour Through the Visualization Zoo
- [*] Shneiderman, The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations
- Card and Mackinlay, The Structure of the Information Visualization Design Space
- Yi, Kang, Stasko, Jacko, Toward a deeper understanding of the role of interaction in information visualization
Oct 13: Multidimensional Visualizations, Maps, and Networks
- [*] Bajaj, Data visualization techniques (Chapters 8)
- [*] Shneiderman and Plaisant, Treemaps for space-constrained visualization of hierarchies
- [*] Network Visualizations from Flowing Data
- 80 Data Visualization Examples Using Location Data and Maps