L1: Huamin Qu
Office hours (in Room 2529): by email appointment.
Ming Yao (firstname.lastname@example.org)
Time and Venues
L1 Friday 7:30 – 10:20pm Room 4619
Final Exam (30%)
In-Class Exercise (10%)
Final project (50%)
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
Interactive Data Visualization: Foundations, Techniques, and Applications by Matthew Ward, Georges Grinstein, and Daniel Keim
The visualization handbook
Information visualization: perception for design
The visual display of quantitative information
Visual explanations: images and qauntities, evidence and narrative
visualcomplexity.com | 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)
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.
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