1. Both undergraduates and graduates are welcome to attend.
2. Topics
2.1. Overview of the Field of Data Visualization
2.2. History of Data Visualization
2.3. Tools used for the class
2.4. Process of Data Visualization
2.5. Best practices in Information Visualization
2.6. Geospatial Visualization
2.7. Privacy Issues in Data Visualization
2.8. Uncertainty in Visualization
2.9. Social Network Visualization
2.10. Working with Data
2.11. Visual Analytics
2.12. Scientific and Medical Visualization
2.13. Dynamic Data Visualization
2.14. Visualization Hardware
3. Projects
3.1. Project 0 (10 points)
3.1.1. Each week 3 students will introduce a visualization found on the web for the lab to critique.
3.1.2. Critique will last no more than 5 minutes each.
3.1.3. Critique will explain:
3.1.3.1. Who developed the visualization
3.1.3.1.1. What organization
3.1.3.1.2. Where is the organization
3.1.3.2. What data is being visualized
3.1.3.3. Where was data obtained
3.1.3.4. How was the visualization created
3.1.3.4.1. What kind of visual representations
3.1.3.4.2. What technical infrastructure (Leaflet, Plotly, etc)
3.1.3.5. Why was the visual representation good / bad
3.2. Project 1 (20 points)
3.2.1. Project to produce a visualization using Plot.ly
3.2.2. Data will be chosen by Jason.
3.3. Project 2 (20 points)
3.3.1. Select a Visualization framework and give a presentation in class
3.3.1.1. Three.JS
3.3.1.2. Vega
3.3.1.3. VegaLite
3.3.1.4. D3
3.3.1.5. Google Charts
3.3.1.6. Chart.JS
3.3.1.7. Springy
3.3.1.8. Processing
3.3.1.9. Scene.JS
3.3.1.10. WebGL
3.3.1.11. Shiny
3.3.1.12. Wrld.js
3.3.1.13. Bokeh
3.3.1.14. Mapbox
3.3.2. or select a paper from the last 5 years of IEEE Visualization conference or related visualization conference to present
3.3.2.1. E.g. Text Visualization
3.4. Project 3 (50 points)
3.4.1. Group Project to produce a visualization poster from scratch using your own data set and chosen API.
3.4.2. Take a visualization technique and attempt to implement it from scratch.
4. What's the Class About
4.1. The objective is to introduce students about the broad field of data visualization, and relevant software tools, so that they are able to obtain basic mastery of the skills needed to: 1. turn raw data into effective visualizations of the data, 2. evaluate the efficacy of visualizations.
5. Who Can Attend
5.1. Students from disciplines other than computer science are very welcome to attend and are encouraged to bring their data.
5.2. Having some programming experience helps but is not absolutely necessary.
5.3. Email: [email protected] to get a class override.
6. How is the Class Structured
6.1. First half of each class involves Jason lecturing on a visualization topic.
6.2. Second half involves students presenting about
6.2.1. A data set they are interested in visualizing
6.2.2. A visualization framework they would like to use
6.2.3. Progress update on projects 1 and 3.
6.3. Teams will be multi-disciplinary- ideally including computer scientist, domain scientist, artist etc.
7. Who's Teaching the Class
8. Where / When is the Class Taught
8.1. Class will be taught in the CyberCANOE in the Cyber Infrastructure Lab- Keller 103
8.2. Class is held on Wednesdays from 3-5:30p
9. Grading
9.1. There are no exams.
9.2. This class is about what you can DO, not what you can memorize so grades will be determined by the work you put into the Projects.
10. Resources
10.1. Tools
10.1.1. 50K Foot View of the Data Visualization Process
10.1.2. Plot.ly
10.1.2.1. Plotly Main site
10.1.2.2. Plotly Javascript site
10.1.2.3. Dash
10.1.2.4. Chart Studio
10.1.3. Leaflet
10.1.4. The Visualization Toolkit
10.1.4.1. VTK.js
10.1.4.2. VTK book
10.1.4.3. VTK User Guide
10.1.4.4. ParaView
10.1.5. Color Brewer
10.2. Data
10.2.1. Kaggle
10.2.2. Hawaii Data
10.2.3. COVID