Design Theory for Data Visualization

This intermediate design-for-data-visualization course explores the intersection of design principles and cognitive psychology and shows you how to create impactful data visualizations. Explore data visualization design through a cognitive and psychological lens to better understand how we perceive and interpret visual information. Along the way, learn about common pitfalls, such as cognitive biases and design errors, as well as how to cater to diverse audiences. Balancing theory with practical applications, instructor Christina Stathopoulous prepares you to develop the knowledge and skills you need to take your data visualization to the next level.

Learning objectives
Analyze the cognitive and perceptual foundations of data visualization by evaluating how human visual perception and psychological principles (such as Gestalt psychology and preattentive attributes) influence the effectiveness of visual data representations.
Create sophisticated data visualizations that adhere to fundamental design principles, strategically applying techniques such as visual hierarchy, appropriate color theory, typography, and the data-ink ratio to communicate complex information clearly and compellingly.
Evaluate the design quality of data visualizations by critically assessing common visualization pitfalls, including problematic axis treatments, dimensional misrepresentations, and potential cognitive biases that can distort data interpretation.
Design audience-centered data visualizations that demonstrate adaptability across different contexts, incorporating accessibility considerations and tailoring visual communication strategies to meet the specific needs of diverse audience groups.
Apply systematic design rules and principles to transform raw data into meaningful, accurate, and visually engaging representations that prioritize simplicity, comprehensiveness, and precise data communication.
Skills covered