Course Contents
Unit 1: Introduction to Visualization [6 Hrs]
Introduction of visual perception, Visual representation of data, Data Abstraction, Visual Encodings, Use of Color, Perceptual Issues, Information overloads.
Unit 2: Creating Visual Representations [7 Hrs]
Visualization reference model, Visual mapping, Visual analytics, Design of Visualization applications.
Unit 3: Non spatial data Visualization [15 Hrs]
Visualization of one, two and multi-dimensional data, Tabular data, quantitative values (scatter plot), Separate, Order, and Align (Bar, staked Bar, dots and line charts), Tree data, Displaying Hierarchical Structures, graph data, rules for graph drawing and labeling, text and document data, levels of text representation, visualizations of a single text document, word cloud, flow data.
Time series data, characteristics of time data, visualization time series data, mapping of time
Unit 4: Spatial Data Visualization [10 Hrs]
Scalar fields, Isocontours (Topographic Terrain Maps), scalar volumes, Direct Volume Rendering (Multidimensional Transfer Functions) , Maps(dot, pixel), vector fields
Defining Marks and Channels
Unit 5: Software tools and Data for Visualization [10 Hrs]
The iris data set, The Detroit Data Set, The Breakfast Cereal Data Set, The Dow Jones Industrial Average Data set (time series), MS Spreadsheet, Python, Matlab, Java, Tableau
Laboratory Work
Laboratory work should be done covering all the topics listed above and a small project work should be carried out using the concept learnt in this course using any one software tools mention in unit 5.
