CIS 4930/6930-902: Scientific Visualization (Fall 2015)

Location: ENG 3
Time: T/H 5:00-6:15 pm

Instructor: Paul Rosen
Office: ENB 311
Office Hours: T/H 4:00-5:00 pm

Course Description

This course will introduce students to the principles and algorithms necessary for effective visual analysis of data. Students will explore many aspects of visualization, including those for spatial (e.g. CT or MRI) and non-spatial data (e.g. financial data). The course begins with an overview of principles from perception and design, continues with skills for critiquing visualizations, and then focuses on visualization techniques and algorithms for a broad range of data types. Students will acquire hands-on experience using cutting edge visualization systems as well as programming interactive visual analysis tools.

Course Syllabus

Course Work

Projects (70%)
We will 5 projects throughout the semester. The first 4 will serve as “introductions” to visualization technologies. The final will be a longer open-ended project.

  • Project 1 (10%): Visualizing Data with Tableau
  • Project 2 (10%): Visualizing Data with D3.js (using HTML and Javascript)
  • Project 3 (10%): Visualizing Data with Processing (using Java)
  • Project 4 (10%): Visualizing Data with VTK (using Java, Python, C++, etc.)
  • Project 5 (30%): Open-ended project. Take one of these technologies and develop a larger-scale project of your choice.

Reading & Discussion Grade (30%)
Since this is a topics course with a small class size, I’m hoping to engage in discussions throughout the semester. Many of these discussion topics have no right or wrong answer. I’d like you to feel free to share your thoughts. This also means attending class regularly. I will also consider out of class discussion (e-mail, office hours, etc.) in determining this grade.



Week 1
Aug. 24 Lecture: Intro to Visualization
Discussion: Introductions, Course Syllabus, Course Format, and Expectations Sign Up for Project Presentation
Aug. 26 Lecture: Visual Design Sign Up for Project Presentation
Week 2
Sept. 1 Lecture: Data Paper Discussion: Tamara Munzner, Process and Pitfalls in Writing Information Visualization Research Papers Project 1 Handed Out
Sept. 3 Lecture: Visual Encoding Design Review: Charles Minard, “The Greatest Infographic Ever” Napoleon’s Russian Campaign
Week 3
Sept. 8 Lecture: Perception
Paper Discussion: Niklas Elmqvist and Ji Soo Yi, Patterns for Visualization Evaluation
Sept. 10 Lecture: Color Video Discussion: Hans Rosling: The best stats you’ve ever seen Project 1 DUE by start of class!
Week 4
Sept. 15 Discussion:  Visualization Critiques
Presentations: Project 1
Project 2 Handed Out
Sept. 17 Lecture: Tasks & Interaction Discussion: Hans-Jorg Schulz, et al., A Design Space of Visualization Tasks
Week 5
Sept. 22 NO CLASS
Sept. 24 Lecture: Views & Focus+Context
Week 6
Sept. 29 LectureFiltering & Aggregation
Project 2 DUE by start of class!
Project 3 Handed Out
Oct. 1 LectureFoundations Review Project 2 Presentations
Week 7
Oct. 6 Lecture: Tables
Paper Discussion: Andy Cockburn,et al., A review of overview+ detail, zooming, and focus+ context interfaces.
Oct. 8 Lecture: Tables (cont.)
Week 8
Oct. 13
Lecture: Trees & Graphs
Project 3 DUE by start of class!
Project 4 Handed Out — Prerequisite, Project Document, Data & Source
Oct. 15 Lecture: Trees & Graphs (cont.) Project 3 Presentations
Week 9
Oct. 20 Lecture: Text Paper Discussion: Elzen and van Wijk, Multivariate Network Exploration and Presentation: From Detail to Overview via Selections and Aggregations (video)
Oct. 22 Lecture: Text (cont)
Week 10
Oct. 27 No Class – use the time to complete project  4
Oct. 29 Lecture: Sets
  Project 4 DUE by start of class!
Week 11
Nov. 3 LectureMaps
Final Project Topic Due
Nov. 5 Lecture: Grids Project 4 Presentations  
Week 12
Nov. 10 Lecture: Isocontours and Isosurfaces
Nov. 12 Lecture: Volume Rendering Paper Discussion: Kieffer et al, HOLA: Human-like Orthogonal Network Layout [InfoVis 2015 Best Paper]
Week 13
Nov. 17 Lecture: Transfer Functions Paper Discussion: van den Elzen et al, Reducing Snapshots to Points: A Visual Analytics Approach to Dynamic Network Exploration [VAST 2015 Best Paper]
Nov. 19 Lecture: Vector-Fields Paper Discussion: Schroeder and Keefe, Visualization-by-Sketching: An Artist’s Interface for Creating Multivariate Time-Varying Data Visualizations [SciVis 2015 Best Paper]
Week 14
Nov. 24 No Class! (Tensor Visualization Slides)
Nov. 26 No Class (Thanksgiving)
Week 15
Finals Week (Dec. 5-11) – NO FINAL!!