Data Visualization - Winter 2018

Instructor: Amit Chourasia, San Diego Supercomputer Center, UCSD

Teaching assistant: Sumit Binani, Computer Science and Engineering, UCSD

Textbook Visualization Analysis and Design, Tamara Munzner (A K Peters Visualization Series, CRC Press, 2014)


Day 1 Jan 5, Day 2 Jan 19 , Day 3 Feb 2 , Day 4 Feb 16 , Day 5 Mar 2 , Day 6 - Finals Mar 16


Class schedule

Day 1 (Jan 5)

Morning

Afternoon

Home work

Day 1 Supplement (Jan 6)

Morning (8:30am - noon)

Tutorial by TA


Day 2 (Jan 19)

Morning

  • Guest Lecture - Applying Color Theory to Visualization. Thersa-Marie Rhyne, Computer Graphics and Visualization Consultant. Slides (PDF)

    Abstract: We examine the foundation of color theory and how these methods apply to building effective visualizations. We define color harmony and demonstrate the application of color harmony to case studies. The material presented is from my book on “Applying Color Theory to Digital Media and Visualization”.

  • Colors suppliment - Slides (PDF)
  • Tables - Slides (PDF)

Afternoon

  • Network and Trees - Slides (PDF)
  • Tutorial (TA): Tableau
  • Exercise 2 using Tableau (Time permitting)

Home work


Day 3 (Feb 2)

Morning

Afternoon

  • Final project proposal presentations (By students) Presentation order
  • Network X and Bokeh tutorial (TA)

Home work


Day 4 (Feb 16)

Morning

  • Focus and Context Slides (PDF)
  • Spatial data slides
  • Peer review for Exercise 2 (in class). See review notes here
  • 11am - noon: DSE290 Guest lecture

Afternoon

Home work


Day 5 (Mar 2)

Morning

Afternoon

  • 1:00pm Guest lecture - High performance visualization Dr. David Nadeau, Sr. Scientist, San Diego Supercomputer Center, UCSD.

    Abstract: Traditional plots work well to show detail and short trends when data is small. But as data grows larger, plot visual complexity and drawing times increase. New visual designs are needed to clearly show complex data, and new high performance techniques are needed to draw visualizations quickly. This talk introduces issues in high performance visualization, GPUs, OpenGL, and WebGL, and illustrates them using large 3D graph and volume visualizations.

  • In class exercise

Home work


Day 6 - Final exam (Mar 16)

Student presentations : Final project presentations


Course Grading

- A (Excellent 4.0) >= 90% - B (Good 3.0) >= 80% - C (Fair 2.0) >= 70% - D (Barely passing 1.0) >= 60% - F (Fail) < 60%

Grade calculation will be as follows

Class policy

  • Attendance is mandatory
  • Must complete all exercises
  • Must complete final project

Guest Lecturers

  1. Applying Color Theory to Visualization - Thersa-Marie Rhyne, Computer Graphics and Visualization Consultant