Course Description

Data Analysis and Visualization intends to extend students' knowledge and practice in data analysis and visualization, software, and applications. It provides a broad overview of techniques in the visualization process, a detailed view of visual perception, the visualized data, and the actual visualization, as well as interaction and distortion techniques.

Course Objectives

Upon completion of this course, students should be able to:

1. Explain the concept of visualization in the processing and analysis of data.
2. Develop visualization methods and visualization systems using software applications.
3. Perform creative work in the field of visualization.

Course Contents

1. Introduction to Visualization : 6 Hrs

This unit covers the introduction of visual perception, visual representation of data, data abstraction, visual encodings, the use of color, perceptual issues, and information overloads.

2. Creating Visual Representations : 7 Hrs

Topics include the visualization reference model, visual mapping, visual analytics, and the design of visualization applications.

3. Non-Spatial Data Visualization : 15 Hrs

This unit explores the visualization of one, two, and multi-dimensional data, tabular data, quantitative values (such as scatter plots), and methods for separating, ordering, and aligning data (using bar charts, stacked bar charts, dots, and line charts). It also covers tree data, displaying hierarchical structures, graph data, rules for graph drawing and labeling, text and document data, levels of text representation, visualizations of single text documents, word clouds, and flow data. Additionally, it includes time series data, characteristics of time data, visualization of time series data, and mapping of time.

4. Spatial Data Visualization : 10 Hrs

This unit focuses on scalar fields, isocontours (topographic terrain maps), scalar volumes, direct volume rendering (using multidimensional transfer functions), maps (dot and pixel-based), and vector fields. It also covers defining marks and channels.

5. Software Tools and Data for Visualization : 10 Hrs

The unit introdsuces datasets such as the iris dataset, the Detroit dataset, the breakfast cereal dataset, and the Dow Jones Industrial Average dataset (time series). It also includes tools such as MS Spreadsheet, Python, Matlab, Java, and Tableau.

Evaluation

Laboratory work should cover all the topics listed above. A small project work should be carried out using the concepts learned in this course, employing any one of the software tools mentioned in Unit 5.

### **Text Books**

1. Fry, _Visualizing Data_. O’Reilly Media, 2008, ISBN 0596514557
2. Ware, _Information Visualization: Perception for Design_, 3rd ed. Morgan Kaufmann, 2012

### **Reference Books**

1. Telea, _Data Visualization: Principles and Practice_. A. K. Peters, Ltd, 2007, ISBN 1568813066