In 2026, after nearly 90 years, the Institute of Design was folded into Illinois Tech's School of Design & Society.
This website is an archive of the impactful and inspirational work produced by the ID community before that transition.

László Moholy-Nagy opened the New Bauhaus in Chicago in 1937, treating design as a discipline that was teachable, testable, and consequential. Nine decades on, its scope had widened from the things people use to the systems they depend on: hospitals, government agencies, classrooms, supply chains, digital platforms. Along the way, ID community members developed the published research, methods, and frameworks that designers rely on every day.

Chief among them is human-centered design: close attention to how people actually live, and advocacy for what that research reveals. It shaped designers who are comfortable with complexity and ambiguity: curious, rigorous, inclusive, and stubbornly optimistic about design's ability to change the systems that shape our lives.

To every one of them, who taught, studied, built, critiqued, and stayed late in the studio: thank you, and keep designing.

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Data Literacy

Data Literacy

Introduction to the methods, tools, and techniques for working with quantitative data in the design process.

I used pivot tables today! I work with many [design] grads and they did not have exposure to working with large datasets. This is just one example of how ID’s wide exposure approach has benefited me.
—Jeff Sprague (MDes 2022)

Objective & Outcomes

This course provides an introduction to working with quantitative data in a variety of contexts that a designer may encounter during the course of her career. These topics range from using survey and sensor or analytics data during research phases; projecting value of new design innovations; and some introductory data science topics when designing data-centric experiences.

Typical Schedule

  • Session 1: Basics of Working with Spreadsheets
  • Session 2: Data Types & Structures
  • Session 3: Tableau for Basic Visualizations & Geospatial Data
  • Session 4: Basic Statistics in R
  • Session 5: Budget Basics & Calculating Future Value of Innovations
  • Session 6: Data Ethics & Storytelling with Data
  • Session 7: Course Review & Final Presentations