SCHEDULE OVERVIEW
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.
DETAILED SCHEDULE
| August | |||
| 22 | Class 1 | Course 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 |
| 24 | Class 2 | Data 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 | |
| 29 | Class 3 | Information 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. |
| 31 | Class 4 |  Select Topics: JavaScript and SVGIntroduction to D3 and Simple Statistics | A1 due |
| September | |||
| 5 | Well-being Day | No Class | |
| 7 | Class 5 | Information 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) |
| 12 | Class 6 | Team Formation and First Meetings | A3: Project Status and Proposal Revision assigned A4: Semester Project Deliverables and Presentation assigned |
| 14 | Class 7 | Hands 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 |
| 19 | Class 8 | Information Visualization: Visual Encoding •Read "Interactive Data Visualization (IDV)" by Ward et al. Chapter 4 Visualization Foundations •Read "IV" Chapter 6 Patterns | |
| 21 | Class 9 | Hands On D3: Part 2 | A5 due A6: More D3 assigned |
| 26 | Class 10 | Team Design Workshop •Read Sketching with Data Opens the Mind's Eye •Read "Graphical Perception..." paper by McGill and Cleveland | |
| 28 | Class 11 | Data Representation and Transformation: Basic Wrangling •Read "Research Directions in Data Wrangling" by Kandel et al. | |
| October | |||
| 3 | Class 12 | Hands On D3: Part 3 •Read about D3 Group and Rollup. | A7: Research Article Choices assigned |
| 5 | Class 13 | Data 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 |
| 10 | Class 14 | Hands On Simple Statistics •Re-Read Simple Statistics web page, focusing on API. | A7 due A8: Simple Statistics Exercise assigned |
| 12 | Class 15 | Project Midterms: Present your revised proposal and progress-to-date to the class. Schedule Research Article Presentations | A3 due A9: Research Article Presentation assigned |
| 17 | Class 16 | Data Representation and Transformation: Dimension Reduction, Classification, Clustering •Read Dimension Reduction on Wikipedia. •Read Statistical Classification on Wikipedia. •Read Cluster Analysis on Wikipedia. | |
| 19 | Fall Break | No Class | |
| 24 | Class 17 | No Class; Prof. Gotz attending IEEE VIS. Use time for team meetings. | |
| 26 | Class 18 | User in the Loop: Connecting Analytics and Visualization •Read "We Need Both Exploratory and Confirmatory" by John Tukey | A8 due |
| 31 | Class 19 | Analytical Reasoning: Models •Read "ItP" Chapter 2, pages 33-48 | |
| November | |||
| 2 | Class 20 | Analytical Reasoning: Challenges | |
| 7 | Class 21 | Guest Lecture: Evan Galloway, Sheps Center | |
| 9 | Class 22 | Research Article Presentations (Day 1) | A9 due (for some) |
| 14 | Class 23 | No Class; Prof. Gotz attending AMIA Annual Symposium. Use time for team meetings. | |
| 16 | Class 24 | Collaboration and Dissemination •Read "Storytelling: The Next Step for Visualization" by Kosara and Mackinlay. | |
| 21 | Class 25 | Research Article Presentations (Day 2) | A9 due (for some) |
| 23 | Thanksgiving | No Class | |
| 28 | Class 26 | Guest Lecture: Alex Harding, RTI Center for Data Science | |
| 30 | Class 27 | Final Project Presentations | A4 part 1 (presentation and online demonstration) due (for some) |
| December | |||
| 5 | Class 28 | Final Project Presentations | A4 part 1 (presentation and online demonstration) due (for some) |
| 12 | Final Exam Period | Project 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 |