Syllabus

Course Description

This course provides a hands-on introduction to designing effective data visualizations, covering principles, techniques, and best practices for creating clear, accurate, and impactful visual representations of data.

Learning Objectives

By the end of this course, students will be able to:

Understand why data visualization design matters and its impact on communication
Apply key principles to evaluate and critique data visualizations
Select appropriate visualization types for different data and purposes
Apply perceptual and aesthetic principles to create effective visualizations
Use design tools (Figma) to prototype and refine visualizations
Create and present professional data visualization projects

Chapters

1. Introduction to Data Visualization Design

Explores the importance of data visualization through concrete examples. Emphasizes that effective visualization requires purposeful design, specific skills, and knowledge—not just “showing the data.”

Activity: Students identify and analyze 2 good and 2 bad visualization examples from their field.

2. What Makes a Visualization Good?

Introduces ten key principles for evaluating data visualization quality. Students learn to systematically assess and critique graphs based on clarity, accuracy, design, and communication effectiveness.

Activity: Students re-evaluate the 2 good and 2 bad visualization examples they identified in the first session.

3. Picking the Right Type of Data Visualization

Reviews common visualization types and decision criteria for selecting the appropriate chart for a given data type and purpose.

Exercises: Drawing scenarios to practice matching visualization types to data and questions (cybersecurity and IQ datasets).

Activity: Students re-evaluate the 2 good and 2 bad visualization examples they identified in the first session.

4. The Science of Data Visualization Perception

Examines experimental psychology findings on how humans perceive visual information. Understanding perceptual fundamentals helps avoid critical mistakes and design more effective visualizations.

Activity: Students re-evaluate the 2 good and 2 bad visualization examples they identified in the first session.

5. Introduction to Figma (with Sophie Doublet)

Hands-on introduction to Figma, a collaborative design tool for prototyping data visualizations. Students learn to draft, iterate, and refine visualization designs before implementation.

Activity: Students create a data visualization using Figma, ideally using as a starting point one of the figures identified earlier.

6. The Art of Data Visualization

Explores aesthetic and perceptual design principles that enhance visualization effectiveness:

Gestalt Theory: Proximity, similarity, enclosure, connection, and how viewers naturally group visual elements
Typography: Font selection and how typeface choices convey meaning and emotion
Color: Color theory, palette types (sequential, diverging, qualitative), accessibility, and practical tools

Key principle: Differences in meaning should correspond to visual differences; semantic similarity should match visual similarity.

Activity: Students form groups and select a common data visualization project.

7. Student Projects

Students present their data visualization projects, demonstrating application of course principles and techniques.