The schedule for this class will weave together four key themes. First, we'll discuss a number of core visual analytics concepts and models. Second, espeically early on in the semester, we'll spend time reviewing the D3.js library, a widely used framework for interactive visualization on the web. Third, we'll review recent examples of research in visual analytics from the scientific literature. And finally, we'll have some special guest speakers address the class both in person and via web conference.

Below you'll find a TENTATIVE schedule for the semester. Please note that all topics/dates/etc. are subject to change at any time. I fully expect to adjust our plans multiple times over the course of the semester.



22Class 1Course Overview and Introduction
•Read "Visual Analytics: Definition, Process, and Challenges" by Keim et al.
•Read "Illuminating the Path (ItP)" Executive Summary (Pages 1-18)
A1: Environment Setup assigned
A2: Semester Project Proposal assigned
24Class 2Data Ethics and Responsibility
•Read "Fired for Not Manipulating COVID-19 Data" interview by NPR's Rachel Martin
•Read "How Deceptive are Deceptive Visualizations?" by Pandey et al.
•Read "Racial Bias Found in a Major Health Care Risk Algorithm" by Starre Vartan
29Class 3Information Visualization Overview
Semester Project Orientation
•Read "Visualization Analysis and Design (VAD)" Chapter 1 ('What's Vis, and Why Do It?')
•Read "A Tour Through the Visualization Zoo" by Heer et al.
A1 due - Due date has been delayed to next class.
31Class 4Select Topics: JavaScript and SVG
Introduction to D3 and Simple Statistics
•Read web pages for D3 and Simple Statistics.
•Bring laptop for in-class exercises.
A1 due
5Well-being DayNo Class
7Class 5Information Visualization: Data and Tasks
•Read "VAD" Chapter 2 ('What: Data Abstraction')
•Read "Information Visualization (IV)" by Colin Ware, portions of Chapter 1 on Types of Data/Metadata
A2 due
Problem preferences due Fri. Sept. 8th @ 11am (see Team Formation Process)
12Class 6Team Formation and First MeetingsA3: Project Status and Proposal Revision assigned
A4: Semester Project Deliverables and Presentation assigned
14Class 7Hands On D3: Part 1
•Read the tutorial Let's Make a Bar Chart.
•Read the tutorial Let's Mark a Bar Chart with SVG.
•Read the tutorial D3 Joins: The Basics.
•Read the tutorial More D3 Joins: Enter, Update, Exit.
A5: D3 Exercise assigned
19Class 8Information Visualization: Visual Encoding
21Class 9Hands On D3: Part 2
•Read about D3 Handling Events.
•Read about D3 Transitions.
A5 due
A6: More D3 assigned
26Class 10Team Design Workshop
28Class 11Data Representation and Transformation: Basic Wrangling
•Read "Research Directions in Data Wrangling" by Kandel et al.
3Class 12Hands On D3: Part 3
•Read about D3 Group and Rollup.
A7: Research Article Choices assigned
5Class 13Data Representation and Transformation: Statistics
•Read Elementary Concepts in Statistics (or as a PDF).
•Read Basic Statistics (or as a PDF).
Note that these texts are based on a publicly available online textbook now owned by TIBCO. Click here for the latest version.
A6 due
10Class 14Hands On Simple Statistics
•Re-Read Simple Statistics web page, focusing on API.
A7 due
A8: Simple Statistics Exercise assigned
12Class 15Project Midterms: Present your revised proposal and progress-to-date to the class.
Schedule Research Article Presentations
A3 due
A9: Research Article Presentation assigned
17Class 16Data Representation and Transformation: Dimension Reduction, Classification, Clustering
•Read Dimension Reduction on Wikipedia.
•Read Statistical Classification on Wikipedia.
•Read Cluster Analysis on Wikipedia.
19Fall BreakNo Class
24Class 17No Class; Prof. Gotz attending IEEE VIS. Use time for team meetings.
26Class 18User in the Loop: Connecting Analytics and VisualizationA8 due
31Class 19Analytical Reasoning: Models
•Read "ItP" Chapter 2, pages 33-48
2Class 20Analytical Reasoning: Challenges
•Read "Confirmation Bias" on Wikipedia.
•Read "Groupthink" on Wikipedia.
7Class 21Guest Lecture: Evan Galloway, Sheps Center
9Class 22Research Article Presentations (Day 1)A9 due (for some)
14Class 23No Class; Prof. Gotz attending AMIA Annual Symposium. Use time for team meetings.
16Class 24Collaboration and Dissemination
•Read "Storytelling: The Next Step for Visualization" by Kosara and Mackinlay.
21Class 25Research Article Presentations (Day 2)A9 due (for some)
23ThanksgivingNo Class
28Class 26Guest Lecture: Alex Harding, RTI Center for Data Science
30Class 27Final Project PresentationsA4 part 1 (presentation and online demonstration) due (for some)
5Class 28Final Project PresentationsA4 part 1 (presentation and online demonstration) due (for some)
12Final Exam PeriodProject Deliverables Due by 8am (Start of Exam Time) A4 part 2 (software prototype) due
A4 part 3 (final report) due
A4 part 4 (team evaluations) due