Course information

From Visual Analytics for Business Intelligence
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Faculty Dr. Kam Tin Seong, Associate Professor of Information Systems (Practice)
Course Visual Analytics for Business Intelligence
Course code IS428
Term Year 2012-2013, Term 1
Section G1
Day & Time Thursday 8:15am-11:30am
Venue NSR 2.3, SIS
Teaching Assistant NA


Synopsis

Data analysis and communications can be fun! With visual analytics techniques and tools, everyday data analysts from various disciplines such business, economic, sociology, political science and public policy can now synthesize information and derive insight from massive, dynamic, ambiguous, and often conflicting data without having to deal with complex statistical formulas and programming. Many companies and organization took notice when Gartner cited visual analytics as one of the top five trends transforming business intelligence.

In this course, students learn how to use data visualization and interactive analytic tools and techniques to interact with data of different formats from various sources, explore the expected relationships and discover unexpected correlations and patterns. You will also acquire the skills for building cutting edge visual analytics application based on principles and best practices from graphic design, visual arts, perceptual psychology, cognitive science and interfaces design.

For more detail information and learning outcome please refer to the course design document. [1]


Learning Objectives

Upon successful completion of the course, students will be able to:

  • Understand the basic concepts, theories and methodologies of Visual Analytics.
  • Analyse data using appropriate visual thinking and visual analytics techniques
  • Present data using appropriate visual communication and graphical methods.
  • Design and implement cutting-edge Visual Analytics system for supporting decision making


Basic Modules

This course comprises ten integrated components as shown below:

VAframework.png


Grading Summary

The grading distribution of this course is as follows:

  • Class Participation 15%
  • Quiz 15%
  • Individual Assignments 30%
    • Assignment 1 10%
    • Assignment 2 10%
    • Assignment 3 10%
  • Visual Analytics Project 40%
    • Formulation of ideas and project proposal 5%
    • Postal presentation 10%
    • VA research paper 10%
    • VA application 15%

There will be no mid-term test or final examination for this course.


Visual Analytics Toolkit

In this course students will be expose to and gain hands-on experience on several generic visual analytics toolkit and specialised data visualisation applications. Below are a list of the core software tools for this course.


Commercial Visual Analytics software

JMP Pro

  • JMP home page [2]
  • Discovering JMP [3]
  • JMP Learning Library [4]
  • JMP® for Students 1: Navigation and Use [5]

Tableau

  • Tableau home page [6]
  • Training and Tutorials [7]
  • Visual Gallery [8]

Panopticon

  • Panopticon home page [9]
  • Visual data Discovery [10]
  • Demo Gallery [11]


Data Visualisation design toolkit

D3.js

  • d3.js home page [12] and download [13]
  • D3.js Tutorial [14]
  • Visual Gallery [15]
  • d3.js API Reference [16]
  • d3.js plug-in [17]
  • d3.js Google Group [18]
  • d3.js on Stack Overflow[19]


Specialised Data Visualisation

NodeXL

Gephi