CIS 4930/6930-002: Data Visualization (Spring 2019/2018/2017)
Location: BSN 1201
Time: M/W 3:30pm-4:45pm
Instructor: Paul Rosen
Office: ENB 311
Office Hours: M/W 2-3:30pm
Course Description
This course will introduce students to the principles and algorithms necessary for effective visual analysis of 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 state-of-the-art visualization systems as well as programming interactive visual analysis tools.
Learning Outcomes
Students will demonstrate the ability to:
- Associate visualizations with the foundational components, e.g. data abstractions and visual encodings, that go into their construction.
- Critique the effectiveness of interactive visualizations with respect to task selection, visual encoding choices, and interaction design and implementation.
- Build effective visualizations by evaluating a provided data and user requirements and programming an interface to match those requirements.
Books
- Optional: T. Munzner, Visualization Analysis and Design, A K Peters/CRC Press, ISBN 9781466508910
Course Work
- Project 1 – Visualizing Data with Tableau
- Project 2 – Drawing Basic Charts in Processing
- Project 3 – Drawing Scatterplots
- Project 4 – Adding Interaction
- Project 5 – Drawing Parallel Coordinates
- Project 6 – Creating a Linked-View Dashboard
- Project 7 – Using Aggregations as Derived Attributes
- Project 8 – Force Directed Graph Layout
- Peer Review of Projects (3 reviews per assignment)
- Research Paper Reviews (Grad: 4; Undergrad: 3)
- Research Paper Presentation (Grad Only)
Lecture Topics/Slides
- Lecture 1 – Introduction to Visualization
- Lecture 2 – Introduction to Processing
- Lecture 3 – Introduction to git
- Lecture 4 – Visual Design
- Lecture 5 – Data Abstraction
- Lecture 6 – Visual Encoding
- Lecture 7 – Perception
- Lecture 8 – Color
- Lecture 9 – Task & Interaction
- Lecture 10 – Single View, Multiview, and Focus+Context
- Lecture 11 – Model-View-Controller
- Lecture 12 – Tabular Data
- Lecture 13 – Trees & Graphs
- Lecture 14 – Visualizing Text
- Lecture 15 – Visualizing Sets
- Lecture 16 – Visualizing Maps
- Lecture 17 – Filtering & Aggregation
- Lecture 18 – Histograms & Correlation
- Lecture 19 (part 1) – Machine Learning
- Lecture 19 (part 2) – Machine Learning
- Lecture 20 – Force-Directed Layouts
- Lecture 21 – Descriptive Statistics & Visualization
- Lecture 22 – Topology & Visualization
Schedule
| Jan 7 | Introduction to Visualization | Jan 9 | Working with Processing |
| Jan 14 | Design Due: Project #1 |
Jan 16 | Data and Visual Encoding |
| Jan 21 | No class (MLK) | Jan 23 | Data and Visual Encoding Due: Peer Reviews (Proj #1) |
| Jan 28 | Color & Perception Due: Project #2 |
Jan 30 | Color & Perception Due: Paper Review #1 |
| Feb 4 | Task & Interaction / View & Focus+Context Due: Peer Review (Proj #2) |
Feb 6 | Task & Interaction / View & Focus+Context Due: Project #3 |
| Feb 11 | Visualizing Tables Due: Peer Review (Proj #3) |
Feb 13 | Visualizing Tables / Paper Presentations (x3) |
| Feb 18 | Visualizing Trees & Graphs Due: Project #4 |
Feb 20 | Trees & Graphs / Paper Presentations (x3) Due: Paper Review #2 |
| Feb 25 | Visualizing Text Due: Peer Review (Proj #4) |
Feb 27 | Visualizing Text / Paper Presentations (x3) Due: Project #5 |
| Mar 4 | Visualizing Sets Due: Peer Review (Proj #5) |
Mar 6 | Visualizing Sets / Paper Presentations (x3) |
| Mar 11 | No class (Spring break) | Mar 13 | No class (Spring break) |
| Mar 18 | Visualizing Maps | Mar 20 | Visualizing Maps / Paper Presentations (x3) |
| Mar 25 | Visualizing High Dimensional Data | Mar 27 | High Dimensional Data / Paper Presentations (x3) Due: Project #6 |
| Apr 1 | Visualization & Statistics Due: Peer Review (Proj #6) |
Apr 3 | Visualization & Statistics / Paper Presentations (x3) |
| Apr 8 | Visualization & Machine Learning Due: Project #7 |
Apr 10 | Machine Learning / Paper Presentations (x3) |
| Apr 15 | Visualization & Computational Topology Due: Peer Review (Proj #7) |
Apr 17 | Computational Topology / Paper Presentations (x3) |
| Apr 22 | Flex day / Paper Presentations (x3) Due: Paper Review #3 |
Apr 24 | Recap/Review Lecture |
| Final Exam: Wednesday, May 1, 12:30pm – 2:30pm | |||
All dates and course content are subject to change.