MAS.S70 | Applied Data Visualization

Course Description

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.


Sep 15
Discussing course plans and expectations
Giving a taste of data visualization through presentations and projects
Sep 22
Learning Day 1
Theory: Foundations
Applied: Hands on with D3Plus
Sep 29
Learning Day 2
Theory: Perception
Applied:  Building custom visualizations with D3
Oct 06
Learning Day 3
Theory: Visualization Techniques
Applied: Functional front-ends with React
Oct 13
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)
Oct 27
Demo Day
Presentations: Guided Project Demos
Nov 03
Project Day 1
Theory: Narrative Visualizations & Intro to Creative Projects
Applied: Starting Work on Course Projects: Project Proposals
Nov 10 Veteran Day (No Class)
Nov 17
Project Day 2
Applied: Project Progress Feedback & Office Hours
Nov 24
Project Day 3
Thanksgiving (No Class)
Dec 1
Final Presentations Day 1
Theory: Visualization and Collaboration
Applied: Project Presentations Day 1
Dec 8
Final Presentations Day 2
Theory: Visualization Evaluation
Applied: Project Presentations Day 2 and Wrap Up. Course reflections.


César Hidalgo

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

Oct 06: Visualization Techniques

Oct 13: Multidimensional Visualizations, Maps, and Networks

More readings will be posted, at latest, one week before the deadline.