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Visual Analytics:  2016-2017



Schedule C1 (CS&P)Computer Science and Philosophy

Schedule C1Computer Science

Schedule C1Mathematics and Computer Science

Schedule CMSc in Computer Science



Computer-supported data visualization (commonly referred to as Visualization for short) is a study of transformation from data to visual representations in order to facilitate effective and efficient cognitive processes in performing tasks involving data. Visual analytics is an advanced form of visualization, in which a visualization process features a significant amount of computational analysis and human-computer interaction. Since the term was coined in 2004, visual analytics has become a de facto standard approach to the development of a practical computer-support system for understanding complex and large scale data. This course introduces students to the fundamentals of visualization as a scholarly subject. It presents major concepts in visualization design and methods for algorithmic development, in conjunction with a close examination of several important data analysis and visualization techniques. It provides students with opportunities to explore the latest research topics in visual analytics.

Please note: course materials and further resources are available on Prof. Chen's website:

Learning outcomes

Students satisfying the prerequisites are expected to:

• understand the purpose of visualization in general and visual analytics in particular;

• be conversant with a collection of visualization and analysis techniques;

• gain confidence and competence in performing data analysis and visualization tasks;

• appreciate the uses and importance of visualization in data-intensive applications;

• appreciate the fundamental role of perception and cognition in visual analytics; and

• engage in discussions on the latest theoretical research topics.


Students taking this course should have acquired essential knowledge of computer science including basic concepts in discrete mathematics, probability, and algorithm complexity. Students are also expected to be competent in programming, and if required, have the ability to learn a simple programming language (e.g., JavaScript) quickly.


This course covers a broad range of topics in visualization (at different levels of detail). Students will be exposed to a variety of visual representations, including basic statistical graphics; popular representations such as tag clouds, treemaps and parallel coordinates; pixel-, glyph-, graph- and map-based representations; visual representations of scalar, vector and tensor fields; and visual representations of temporal or spatiotemporal data.

In particular, students will study the methodology of formulating a visual analytics pipeline by combining interactive visualization with analytical techniques (e.g., filtering, clustering, and  dimensionality reduction), and will gain an understanding of the fundamental concept that interactive visualization helps break the conditions of data processing inequality, which is a constraint typically imposed on most automated analytical processes.

The course will help students to answer questions such as:

  • What is visualization really for?
  • What are the relative merits and limitations of some important techniques in visual analytics?
  • What are the theoretical and technical challenges to be addressed?


  1. "Hello, visualization." main concepts, subject overview, and hand-on experience.
  2. Visualization from different perspectives (a) data-centric view, (b) task-centric view, (c) domain-centric view, and (d) information theoretic view.
  3. Technical case study 1: Parallel coordinates visualization.
  4. Visual mappings and perceptual considerations.
  5. Technical case study 2: Volume visualization.
  6. Technical case study 3: Video visualization.
  7. Technical case study 4: Classification and clustering.
  8. Visual analytics pipelines: statistics, algorithms, visualization, interaction... 
  9. Technical case study 5: Dimensionality reduction.
  10. Technical case study 6:Trees and graphs.
  11. Advanced topics: (a) visualization taxonomy, (b) theory of visualization, (c) quality of visualization, and (d) knowledge-assisted visualization.

Reading list

Course Texts

Matthew Ward, Georges Grinstein, and Daniel Keim. Interactive Data Visualization: Foundations, Techniques, and Applications, AK Peters, 2010.

Alexandru C. Telea, Data Visualization: Principles and Practice, AK Peters, 2008.

Pang-Ning Tan, Michael Steinbach and Vipin Kumar, Introduction to Data Mining, Pearson, 2014.

Further Readings:   

Colin Ware, Information Visualization: Perception for Design, Morgan Kaufman, 2004.

Tamara Munzner, Visualization Analysis and Design, AK Peters, 2014.

Robert Spence, Information Visualization: Design for Interaction, 2nd Ed., Prentice Hall, 2006.

James J. Thomas and Kristin A. Cook (eds.), Illuminating the Path: The Research and Development Agenda for Visual Analytics. IEEE Computer Society, 2005. PDF Online.

Mike Dewar, Getting Started with D3, O'Reilly Media, 2012.

Stéphane Tufféry, Data Mining and Statistics for Decision Making, Wiley, 2011.

Other General References

Charles Hansen and Christopher Johnson, The Visualization Handbook, Academic Press, 2005.

Stuart K. Card, Jock D. Mackinlay, and Ben Shneiderman, Readings in Information Visualization: Using Vision to Think, Morgan Kaufmann, 1999.

Jacques Bertin, Semiology of Graphics, Esri Press, 1983.

Jeffrey Heer, Michael Bostock, and Vadim Ogievetsky, “A Tour through the Visualization Zoo”, Communications of the ACM, 53(6):59-67, 2010. PDF Online.

Tableau Software (Pat Hanrahan, Chris Stolte, Jock Mackinlay), Visual Analysis for Everyone: Understanding Data Exploration and Visualization, 2007. PDF Online.

William Schroeder, Ken Martin, Bill Lorensen, The Visualization Toolkit: An Object-Oriented Approach to 3D Graphics, 2nd Ed., 1997.

Edward R. Tufte, The Visual Display of Quantitative Information, 2nd Ed., Graphics Press, 2001.

Stephen Few, Now You See It, Analytics Press, 2009.

Morris H. DeGroot, Mark J. Schervish, Probability and Statistics, Pearson, 2011.

A small collection of research papers, software and web sites will also be recommended to students during the course.