Course Title: 
Data Visualization
Course Code: 
Year Taught: 
Postgraduate (PG)
School of Engineering

'Data Visualization' is an elective course offered in M. Tech. in Computer Science and Engineering program at School of Engineering, Amrita Vishwa Vidyapeetham.

Value of Visualization – What is Visualization and Why do it: External representation – Interactivity – Difficulty in Validation. Data Abstraction: Dataset types – Attribute types – Semantics. Task Abstraction – Analyze, Produce, Search, Query. Four levels of validation – Validation approaches – Validation examples. Marks and Channels

Rules of thumb – Arrange tables: Categorical regions – Spatial axis orientation – Spatial layout density. Arrange spatial data: Geometry – Scalar fields – Vector fields – Tensor fields. Arrange networks and trees: Connections, Matrix views – Containment. Map color: Color theory, Color maps and other channels.

Manipulate view: Change view over time – Select elements – Changing viewpoint – Reducing attributes. Facet into multiple views: Juxtapose and Coordinate views – Partition into views – Static and Dynamic layers – Reduce items and attributes: Filter – Aggregate. Focus and context: Elide – Superimpose - Distort – Case studies.


  1. Tamara Munzner, Visualization Analysis and Design, A K Peters Visualization Series, CRC Press, 2014.
  2. Scott Murray, Interactive Data Visualization for the Web, O’Reilly, 2013.
  3. Alberto Cairo, The Functional Art: An Introduction to Information Graphics and Visualization, New Riders, 2012
  4. Nathan Yau, Visualize This: The FlowingData Guide to Design, Visualization and Statistics, John Wiley & Sons, 2011.

At the end of the course the students will be able to

  Course Outcome Bloom’s Taxonomy Level
CO 1 Understand the key techniques and theory behind data visualization L2
CO 2 Use effectively the various visualization structures (like tables, spatial data, tree and network etc.) L3
CO 3 Evaluate information visualization systems and other forms of visual presentation for their effectiveness L4, L5
CO 4 Design and build data visualization systems L4, L5