Overview:

A hands-on introduction to data visualization. The course is structured around a series of projects in which students will practice skills learned in class. The classroom format will include lectures, discussion, tutorials on visualization tools, and full-class project critiques.

Career Focus:

This course is designed for students who expect to analyze or present data during their career—and these days, data is everywhere, from finance to management consulting. For anyone working with complex information, visualization is a power tool that can drive insight and understanding. It can also be a compelling ingredient in clear and persuasive communication.

The course will also be useful for students who are planning on managing or advising teams that work with data. We will discuss how to critically evaluate visualizations, and how to use them as a bridge between quantitative analysis and decision-making.

Because the course will require significant hands-on work with data, students should have taken DSM (or have equivalent expertise).

Educational Objectives:

Students will learn to use data visualization as a tool to gain actionable insights from data, and then communicate those insights to others. The format will be a departure from the standard case study method. Instead, we’ll focus on projects that require building visualizations as well as extended in-class critiques.

Objectives include:

• An understanding of the basic theory behind data visualization: perceptual science, design, and technical underpinnings, as well as how to match different techniques to different data sets.
• The ability to create visualizations using such tools as spreadsheets, Tableau, and AI-based systems
• Knowledge of how to apply visualization techniques for both exploratory data analysis and communication of data
• Ability to critique visualizations made by others, and understand common pitfalls that can lead to ineffective or even deceptive visualizations

Course Content and Organization:

Course organization and exact number of projects may vary, and we aim to have at least one expert outside speaker. However, here is the basic structure of the course.

Visualization fundamentals

• Analysis of complex visualizations, and what insights they can provide
• How to use psychological science to enhance visualization efficacy
• Intro to visualization tools
• Themes: analysis vs. communication

Communication

• Graphic design principles
• Telling a story with visualization
• Visualization tools: annotation and illustration
• Project: create a visualization presentation that sends a message

Analysis and Control

• Boston CityScore case
• Dashboard design
• Visualization tools: testing hypotheses
• Project: create a dashboard using Boston city data

Exploration with Visualization

• Finding hidden and unexpected aspects of a data set
• Uncovering errors, and how to clean data
• Visualization tools: interactivity
• Project: Explore data from a domain where you have expertise, then present a surprising result to your classmates

Grading / Course Administration:

Weekly homework will involve building and critiquing data visualizations. In some cases students will be expected to find, clean, or prepare their own data sets. There will be a two-week final project.

Grading is based on:

• 65% project work
• 35% class participation







Copyright © 2023 President & Fellows of Harvard College. All Rights Reserved.