Data Analysis and Visualization Syllabus | Semester 8 | BCATU

Data Analysis and Visualization Syllabus | Semester 8 | BCATU

Course Syllabus

1 Doc

0 views5 Units48 Teaching Hrs

Course Information

Type: Theory
Course Code: CACS455
Credit Hours: 3
Full Marks: 100
Pass Marks: 40

Course Description

Data Analysis and Visualization intends to extend student’s knowledge and practice in data analysis and visualization, software, and applications. It provides the board overview of techniques of the visualization process, detailed view of visual perception, the visualized data and the actual visualization, interaction and distorting techniques.

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 Units

5 Units · 48 Hrs

Introduction of visual perception, Visual representation of data, Data Abstraction, Visual Encodings, Use of Color, Perceptual Issues, Information overloads.

Visualization reference model, Visual mapping, Visual analytics, Design of Visualization applications.

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

Scalar fields, Isocontours (Topographic Terrain Maps), scalar volumes, Direct Volume Rendering (Multidimensional Transfer Functions) , Maps(dot, pixel), vector fields
Defining Marks and Channels

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 Works

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.

References & Books

Fry, Visualizing Data. O’Reilly Media, 2008, ISBN 0596514557
Ware, Information Visualization: Perception for Design, 3rd ed. Morgan Kaufmann, 2012
Reference Books
Telea, Data Visualization: Principles and Practice. A. K. Peters, Ltd, 2007, ISBN 1568813066.

Course Overview

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