COMP4462 (L1) - Data Visualization

COMP4462 (L1) - Data Visualization

Spring 2018   Lecture Notes Lab Schedule Course Projects   Announcements 



Huamin Qu
Office hours (in Room 2529): by email appointment.



LO Yu Ho ( )

Yao Ming ( )


Time and Venues


WeFr 04:30PM - 05:50PM Rm 1104, Acad Concourse


LA Tu 05:00PM - 05:50PM  Rm 4210, Lift 19


Grading Scheme:

Final Exam (30%) (Exam)
Top Vis Competition and Essay (10%) (Competition, Essay)

In-Class Exercise and Lab (10%) (In-Class Exercise,  Lab)  
Final project (50%) (Project)

Course Description:

This course will introduce visualization techniques for data from everyday life, social media, business, scientific computing, medical imaging, etc. The topics include human visual system and perception, visual design principles, open- source visualization tools and systems, visualization techniques for CT/MRI data, computational fluid dynamics, graphs and networks, time-series data, text and documents, Twitter data, and spatio-temporal data. The labs and the course project will give students hands-on experience to turn their favorite data into beautiful visualizations.



  • Visualization Analysis and Design by Tamara Munzner: Visualization Analysis and Design
  • Interactive Data Visualization: Foundations, Techniques, and Applications by Matthew Ward, Georges Grinstein, and Daniel Keim: Interactive Data Visualization
  • The visualization handbook
  • Information visualization: perception for design
  • The visual display of quantitative information
  • Envisioning information
  • Visual explanations: images and qauntities, evidence and narrative

Similar Courses:

Data Visualization from University of Washington

Visualization from Harvard

Information Visualization from University of British Columbia



Resources:  | A visual exploration on mapping complex networks
information aesthetics - Information Visualization & Visual Communication


Conferences and Journals:

IEEE VIS Conferences
IEEE Transactions on Visualization and Computer Graphics
IEEE Xplore (Proceedings of IEEE visualization, infovis; TVCG)



Exam Scheduling:  

Final   TBD  TBD  TBD



Intellectual Honesty & Plagiarism

  • Students are expected to follow the HKUST Academic Honor Code
  • All work submitted for grading, e.g., assignments, must be your own
  • You are permitted to discuss problems with other students but you must write-up all solutions by yourself, in your own words.
  • If you got the main idea for a solution from another student or a web-site you must acknowledge that source in your submission.
  • Submission of non-acknowledged material will be considered as plagiarism and dealt with under the university policy for cheating.


Course Summary:

Date Details