Course Design Document. IS428: Visual Analytics for Business Intelligence. Version 1.2

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Course Design Document

IS428: Visual Analytics for Business

Intelligence

Version 1.2

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Table of Content

1. Versions History ...4

2. Visual Analytics for Business Intelligence: An Overview ...5

2.1 Synopsis ... 5

2.2 Basic Modules ... 6

2.3 Objectives ... 7

2.4 Prerequisites ... 7

2.5 Who should attend ... 7

3. Output and Grading Summary ...9

3.1 Class Participation ... 9

3.2 Individual Assignments ... 9

3.3 Visual Analytics Project ...10

3.4 Mid-term test and Final Examination ...10

4. Course Organisation ... 11

4.1 Class Preparation ...11

5. Course Schedule Summary ... 12

6. List of Information Resources and References ... 13

6.1 Recommended Text ...13

7. Tooling ... 14

8. Weekly Plan ... 15

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1. Versions History

Version

Description of Changes

Author

Date

Version 0 Creation of initial document Kam Tin Seong 20-03-2009 Version 1 Revision following comments and

advise from Steven Miller

Kam Tin Seong 1-05-2010 Version 1.1 Revision with reference to

example given in course design document of IS305

Kam Tin Seong 11 June 2010

Version 1.2 Revision by incorporating

students’ feedback Kam Tin Seong 6

th

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2. Visual Analytics for Business Intelligence: An Overview

2.1 Synopsis

Visual analytics is the science of analytical reasoning facilitated by interactive visual interfaces. [Thomas and Cook, 2006]. People use visual analytics tools and techniques to: synthesize information and derive insight from massive, dynamic, ambiguous, and often conflicting data, provide timely, defensible, and understandable assesments; and communicate assesment effectively for action. The overall goal is to detect the expected and discover the unexpected.

Visual analytics has a large and growing spectrum of application areas ranging from commercial (finance, business, medical/health care, insurance), law enforcement (money laundering, capital crimes), homeland security (combat terrorism, border security), national security (intelligence, information access) to information technology (internet security, network analysis, software management and debugging, etc).

It is a multidisciplinary field that includes the following focus areas:

 Analytical reasoning techniques that enable users to obtain deep insights that directly support assesment, planning, and decision making

 Visual reprentations and interaction techniques that take advantage of the human eye's broad bandwidth pathway into the mind to allow users to see, explore, and understand large amounts of information at once

 Data representations and transformations that convert all types of conflicting and dynamic data in ways that support visualization and analysis

 Computation graphics and information dashboard design techniques to support production, presentation, and dissemination of the results of an analysis to communicate information in the appropriate context to a variety of audiences.

The goals of this course are for students: to develop a comprehensive understanding of this emerging, multidisciplinary field, and to apply that understanding in building cutting-edge visual analytics tools and systems using real world data. The latter may involve: advancing the theory of visually-enabled analytical reasoning, developing new methods to support analytic tasks in specific domains, applying existing methods and tools to analytic challenges in these domains, or evaluating and improving the usefulness and usability of visual analytics applications.

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2.2 Basic Modules

This course comprises seven integrated components as shown below:

Foundation for a Science of Visual Analytics  Demystifying visual analytics

 Milestones tour of visual analytics

 Perception, cognition and visual reasoning  Analytics discourse and visual analytics  A gallery of visual analytics applications Visual Analytics Methodologies and Techniques

 Semiology of graphics (Jacques Bertin, 1967)  Theory of data graphics (Edward R. Tufte, 19 )

 Exploratory Data Analysis (EDA) (John Wilder Tukey, 1977) and interactive graphics

 Interactive visualisation for high-dimensions  Space-constrained visualization of hierarchies  Time on the Horizon

 Graph Visualisation

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Visual Analytics Tools and Software

 Conventional computation graphic tools  Research-based visual analysis tools  Commercial visual analytics software  Open source visual analytics toolkits Designing Great Visual Analytics Systems

 System Design Principles and Best Practices

 Development Environment and Software Architecture  Database Integration Strategies

 Systems Deployment Options

 Design for Asynchronous Collaborative

 Users Experiences and Usability Considerations Visual Analytics in Actions

 Information Dashboard Reporting Systems  Network Security and Intrusion Detection  Visualising Social Media

2.3 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

2.4 Prerequisites

Basic computer skills will be assumed. Students are expected to understand Windows-based operating systems and to manage files and disk space responsibly.

There are no prerequisites for the class and the class is open to SIS students as well as non-SIS students. However, a basic working knowledge of, or willingness to learn, a graphics API (e.g., Flare for Flex, AXIIS for Flex, Protovis) and Visual Analytics tools (e.g., Tableau, JMP, Panopticon) will be useful.

2.5 Who should attend

This course is designed for two audiences—IS students and non-IS students majoring in business, accounting, law, economic and social sciences. Both groups of students will be exposed to visual analytics technologies and gains hands-on experiences on visual analytics tools and programmes. When come to project, IS students are encouraged to focus on topics related to (i) the integration

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of visual analytics tools with enterprise information systems, (ii) design and development of visual analytics tolls, or (iii) enhance the analytical and visualization functions of existing visual analytics tools. The NIS students, on-the-other-hand, are encouraged to apply visual analytics tools or techniques in their area of study.

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3. Output and Grading Summary

The grading distribution of this course is as follows:

 Class Participation 15%

 Individual Assignments 40%

 Assignment 1 10%

 Visual Analytics Project 45%

 Formulation of ideas and project proposal 10%

 Postal presentation 15%

 Application report & Solution 20%

3.1 Class Participation

A strict requirement for each class meeting is to complete the assigned readings and to try out the hands-on exercises before coming to class. Readings will be provided from the textbook on technical information and from provided documents and articles on business applications of Visual Analytics. Students are required to review the recommended readings and class exercises before coming to class. Without preparation, the learning and discussions would not be as meaningful. Student sharing of insights from readings and hands-on exercises of assigned materials in class participation will form a large part of the learning in this course. In this course class participation includes participation in the discussion on course

wiki. All students are required to post at least one substantive discussion

comment or question pertaining to each lesson, set of readings, and hands-on exercise. Comments or questions for each lesson must be posted within one week after the lesson.

Examples of good comments include and not confine to the followings:  Clarification of some points or details presented in the class

 Links to web resources or examples that pertain to a lesson or reading with reasons

 Question about the readings or answers to other peoples questions  Reflection on skills learnt through working on an hands-on exercise.

3.2 Individual Assignments

There are three assignments that are due throughout the term. Students may work together to help one another with computer or Visual Analytics issues and discuss the materials that constitute the assignment. However, each student is required to prepare and submit the assignment (including any computer work) on their own. Cheating is strictly forbidden. Cheating includes but not limited to: plagiarism and submission of work that is not the student’s own.

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All assignments due are to be uploaded into the Assignment Dropbox strictly before the official due dates. Late work, will be severely penalised. Students must check and confirm on Wiki the assignment due dates.

The assignments will be graded on a scale from 0 to 10. Scores of 7 and 8 are given when the assignment is essentially done completely and correctly. Scores 9 and 10 are reserved for complete and correct homework where extra initiative or innovation clearly sets the completed work above the simple, perfunctory and satisfactory completion of the assignment.

3.3 Visual Analytics Project

The purpose of the project is to provide students first hand experience on collecting, processing and analysing large business data using real world data. A project may involve developing new methods or implementing visual analytics system to support analytic tasks in specific domains. Alternatively, a project may be in the form of application development by integrating analytical tools within a visual analytics environment. Students are encouraged to focus on research topics that are relevant to their field of study. It should address a concrete visual analytics problem and should propose a novel and creative solution.

The project is team work. Students are required to form a project team of 2-3 members by the third week of the academic term. Each project teams must start thinking about their project ideas after the first lesson. They are expected to discuss their project topic and scope of works with the instructor during the second week of the academic term. A project website will be prepared and submitted to the instructor for approval by week 7.

Each project team will be responsible for presenting the project twice. The initial presentation (week 9) should describe the visualization problem that the project will address, the relevant related work, the approach the team plans to take to solve the problem, and early prototypes or storyboards. The project teams should take advantage of this presentation as a chance to get feedback on the direction of the project from their peers.

All project teams will give a poster presentation outlining the motivation of the project, design principles, implementation process, analytical methods used and findings of their project in week 13. Students are also required to submit a research paper of not more than 12 pages (excluding maps, figures, and tables) in the format of a conference paper submission in week 15.

Additional materials will be uploaded into course wiki and explain in class to assist students with topics selection, project design, postal presentation, and research paper writing.

3.4 Mid-term test and Final Examination

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4. Course Organisation

There is one session of three hours lesson in each week. The weekly lessons include both theoretical or/and technical discussions of Visual Analytics technology and hands-on exercises that focus on business related issues which use a Visual Analytics tools to analyse data or solve a problem. Through weekly discussion and hands-on exercises studies students will not only learn how to use the Visual Analytics techniques or/and tools but will also learn the many distinctive advantages of using Visual Analytics techniques or/and tools for business decision making and strategic planning.

4.1 Class Preparation

Students must bring their personal notebook computer to class, each and every time. Graphical presentation and data analysis tend to consume a lot of RAM. You should have at least 2Gb of RAM installed in your laptop computer. In this course large database (i.e. 1Gb and above) will be used. It is strongly recommended that students carry an external hard disk if the storage capacity of the hard disk of the notebook computer is running low.

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5. Course Schedule Summary

Week Topics Date Events

1 Demystifying Visual Analytics

2 Show Me the Number: Designing

Graphs to Enlighten

3 Interactive and Dynamic Graphics for

Data Analysis

4 Visualising and Analysing

High-dimensions Data

Due: Assignment 1

5 Space-constrained Visualization of

Hierarchies

6 Time on the Horizon

7 Graph Visualisation Due: Assignment 2

8 Recess Break

9 Initial Project Presentation

10 Text and Document Visualisation Due: Assignment 3

11 Information Dashboard Design

12 Designing Great Visual Analytics

Systems

13 Project presentation Due: Project report

14 Study Week

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6. List of Information Resources and References

6.1 Recommended Text

Chen, C.H., Hardle, Wolfgang, and Unwin, Antony (eds) (2008) Handbook of

Data Visualization, Springer-Verlag, Berlin Heidelberg.

Cook, D and Swayne, Deborah F. (2007) Interative and Dynamic Graphics for

Data Analysis, Springer Science+Business Media, LLC. New York.

Few, Stephen (2006) Information Dashboard Design: The Effective

Communication of Data, O’Reilly Media, Inc. Sebastopol, USA.

Few, Stephen (2004) Show Me the Numbers: Designing Tables and Graphs

to Enlighten, Analytics Press, Oakland, USA.

Few, Stephen (2009) Now You See It: Simple Visualization Techniques for

Quantitative Analysis, Analytics Press, Oakland, USA.

Mazza, R. (2009) Introduction to Information Visualization, Springer-Verlag,

London.

Robbins, Naomi B. (2005) Creating More Effective Graphs, John Wiley & Sons,

New Jersey, USA.

Spence, Robert. (2007) Information Visualization: Design for Interaction (2nd

Edition), Person Education Limited, Essex, England.

Stuart K. Card, Jock D. Mackinlay, and Ben Shneiderman (1999) Readings in

Information Visualization: Using Vision to Think, Morgan Kaufmann Publishers, San Francisco, CA.

Tufte, Edward R. (2001) The Visual Display of Quantitative Information (2nd

Edition), Graphics Press LLC, Connecticut, USA.

Unwin, Antony, Theus, Martin. And Hofmann, Heike (2006) Graphics of Large

Datasets: Visualizing a Million, Springer Science+Business Media, LLC. New York.

Ward, Matthew., Grinstein, Georges., and Keim, Daniel., (2011) Interactive Data

Visualization: Foundations, Techniques, and Applications, A. K. Peters Ltd. Natick MA, USA.

Ware, Colin (2008) Visual Thinking for Design, Morgan Kaufmann, San

Francisco, USA.

Ware, Colin (2004) Information Visualization: Perception for Design (2nd

Edition), Morgan Kaufmann, San Francisco, USA.

Wong, Dona M. (2010) The Wall Street Journal Guide to Information

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7. Tooling

 JMP Pro 9, Tableau Public 6.1, TIBCO Silver Spotfire and Panopticon. Students are required to fix an appointment with the course instructor to have the software install in your personal computer one week before the term start.

 Adobe Flash Builder 4.5 and Actionscript data visualisation library such as

flare (http://flare.prefuse.org/)

 JavaScript data visualisation library such as Protovis (http://vis.stanford.edu/protovis/).

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8. Weekly Plan

Week:

1 Date:

Discussion Topics: Demystifying Visual Analytics

Introduction to the course

 Why this course?

 What does it cover?

 Who is involved?

 What assignments?

 Rules to be followed Motivations of Visual Analytics

 Massive data

 Complex problem

 Visual Representation

 New visual paradigm

 Hidden insight

The Visual Analytics Framework

 Components of visual analytics

 History of visual analytics

 The visual analytics process

 Application challenges

 Technical challenges

A Gallery of Visual Analytics applications

Comparing and Evaluating Visualization Techniques

 User tasks

 User characteristics

 Data characteristics

 Visualization characteristics

 Structures for evaluating visualizations

 Benchmarking procedures

Hands-on Exercises:

Visual Analytics for Business Intelligence Workbook Chapter 1: Using Visual Analytics Software

Assignment:

NA

Reading:

James J. Thomas and Kristin A. Cook (eds) (2005) Illuminating the Path: The Research and Development Agenda for Visual Analytics, National Visualization and Analytics Center(NVAC) (http://nvac.pnl.gov/agenda.stm)

Project:

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Week:

2 Date:

Discussion Topics: Foundation for a Science of Visual Analytics

 The Science of Analytical Reasoning

 Sense-Making Methods

Human Perception and Information Processing

 What Is Perception?

 Physiology

 Perceptual Processing

 Perception in Visualization

 Metrics

Perceptual and Design Principles for Effective Visual Analytics

 System, Color, Gestalt Laws, Pre-attentive processing

 Representation: The encoding of value and relation

 Visual Perception and Quantitative Communication Designing Charts to Enlighten

 What we mean by an enlighten graph

 JunkCharts: Understand the limitation of Excel charts

 Principles of Graphic Design

 Semiology of graphics

 Useful Charts for Business Intelligence: Histogram, Line Graph, Bar Chart, Boxplot, Dotplot, Pareto Chart, Scatterplots, Ternary Plots

Data Foundation

 Types of data

 Structure within and between records



Data preprocessing

Hands-on Exercises:

Assignment:

Reading:

Ware, Colin (2008) Visual Thinking for Design, Morgan Kaufmann, San Francisco, USA. Few, Stephen (2004) Show Me the Numbers: Deasigning Tables and Graphs to Enlighten, Analytics Press, Oakland, USA.

Tufte, Edward R. (2001) The Visual Display of Quantitative Information (2nd Edition), Graphics Press LLC, Connecticut, USA.

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Week:

3 Date:

Discussion Topics: Interactive and Dynamic Graphics for Visual Analysis

Interaction Concepts and Framework

 Interaction Operators

 Interaction Operands and Spaces

 A Unified Framework Typology of interaction

 Screen space

 Object-space (3D surfaces)

 Data space (multivariate data values)

 Attribute space (properties of graphical entities)

 Data structure space (components of data organization)

 Visualization structure space (components of the data visualization)

 Animating Transformation Interactive Techniques  Brushing  Identification  Scaling  Subset selection

 Line segments link views

 Dragging points

 Rotating

Pattern Detection and Knowledge Discovery with Interactive Graphics

 Design for interaction

 Case Studies

Hands-on Exercises:

Assignment:

Reading:

Spence, Robert. (2007) Information Visualization: Design for Interaction (2nd Edition), Person Education Limited, Essex, England. Chapter 5 & 6.

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Week:

4 Date:

Discussion Topics: Interactive Visualisation for High-dimensions

 Nature of multivariate data

 Small multiples

 Multiple concurrent views with brushing

 Glyphs or star chart

 TableLens  Parallel Coordinates  Heatmaps

Hands-on Exercises:

Assignment:

Reading: Web resource

 Wikipedia. Parallel Coordinates (http://en.wikipedia.org/wiki/Parallel_coordinates)

 Wikipedia. Heat Map (http://en.wikipedia.org/wiki/Heat_map)

 Home of Parallel Coordinates (http://www.math.tau.ac.il/~aiisreal/)

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Week:

5 Date: 12

Discussion Topics: Space-constrained visualization of hierarchies

 What is so special about hierarchical data

 Space-constrained visualization technique

 Treemap in action

 New variations of treemaps

Hands-on Exercises:

Assignment:

Reading:

Web Resource

 Shneiderman, Ben (2008) Treemaps for space-constrained visualization of hierarchies (http://www.cs.umd.edu/hcil/treemap-history/)

 Kerwin, Thomas. Survey of treemap techniques ( http://www.cse.ohio-state.edu/~kerwin/treemap-survey.html)

 Wikipedia. Treemapping (http://en.wikipedia.org/wiki/Treemap)

Software Tool:

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Week:

6 Date:

Discussion Topics: Time on the Horizon

 Characteristics of time-series data

 Representing time series

 Cycle plots: An effective alternative to time-series line charts

 Sizing the horizon with horizon graph

 Visual queries for detecting patterns from time series data

Hands-on Exercises:



Assignment:

Reading:

Web Resource:

Visual Exploration of Time-Series Data (http://www.cs.umd.edu/hcil/timesearcher/)

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Week:

7 Date:

Discussion Topics: Graph Visualisation

 Data and Graph

 Key issues in graph visualisation

 Graph layouts

 Navigation and interaction

 Clustering

 Typical application areas

 Visualising graphs as trees: Plant a seed and watch it grow

Hands-on Exercises:

Assignment:



Reading:

Herman, Ivan., Melancon, G., and Marshall, M. Scott. (2006) ―Graph Visualization and Navigation in Information Visualization: A Survey‖, IEEE Transactions on Visualization and Computer Graphics, Vol. 6, No. 1. P. 24. (SMU e-journal)

Web Resource

Software Tool

 SpaceTree 1.6: a novel node-link tree browser (http://www.cs.umd.edu/hcil/spacetree/)

 TreePlus: Tree-based Graph Visualization (http://www.cs.umd.edu/hcil/treeplus/index.shtml)

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Week:

8 Date:

Recess Break

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Week:

9 Date:

Discussion Topics: Visual Analytics for Social Media

Social Media Data Analytics

 Issues and challenges

 A Process Model for Analyzing and Visualizing Social Media Data

 Social Network Analysis on the Semantic Web Analysis of Social Network

 Entity rankings  Relationship rankings  Cohensive subgroups  Ego-centric exploration  Applications

 The Name Voyeger

Hands-on Exercises:

Assignment:

Reading:

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Week:

10 Date:

Discussion Topics: Visual Analytics for Large Space-Time-Attribute

Datasets

Visualizing GeoSpatial Data

 Visualization of Point Data

 Visualization of Line Data

 Visualization of Area Data

Issues in Geospatial Visual Analytics

 Challenges on gaining insights from movement data

 Visual analytics methods for movement data

 Data Manipulation

 Patterns detection and visualisation

Hands-on Exercises:

Assignment:

Reading:

Andrienko, G. et. al. (2008) ―Visual Analytics Methods for Movement Data‖, in Giannotti, F. and Pedreschi, D. (eds) Mobility, Data Mining and Privacy. Springer-Verlag Berlin Heidelberg. (SMU e-journal)

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Week:

11 Date:

Discussion Topics: Designing Great Visual Analytics Systems

 System Design Principles and Best Practices

 Development Environment and Software Architecture

 Database Integration Strategies

 Systems Deployment Options

 Design for Asynchronous Collaborative

Hands-on Exercises:

Assignment:

Reading:

Heer J., and Agrawala M. (2006) ―Software design patterns for information visualization‖ IEEE Transactions on Visualization and Computer Graphics, Vol. 12, No.5, p 853.

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Week:

12 Date:

25 March 2008

Discussion Topics: Information Dashboard Design for at-a-Glance

Monitoring

 Even dashboards have a history

 Dashboards design technologies

 Variations in dashboard uses and data

 Common mistakes in dashboards design

 Applying the principles of visual perception to dashboards design

Hands-on Exercises:



Assignment:

Reading:

Few, Stephen (2006) Information Dashboard Design: The Effective Communication of Data, O’Reilly Media, Inc. Sebastopol, USA.

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Week:

13 Date:

01 April 2008

Project Presentation:

Hands-on Exercises:

Assignment:

NIL

Reading:

Project:

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Week:

14 Date:

Revision week

Week:

15 Date:

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9. Learning outcomes, achievement methods and assessment

IS428 – Visual Analytics for

Business Intelligence

Course-specific core competencies which address the Outcomes

Faculty Methods to Assess Outcomes

1 Integration of business & technology in a sector context

1.1 Business IT value linkage skills YY

 Identify the key benefits of visual analytics in an organization

 Identify the role of information graphics and visual analytics systems in an organization and explain major concerns and issues occurring at each of the business processes

 Student’s critics on examples and case studies.

 Grading of assignments and project.

1.2 Cost and benefits analysis skills Y

 Evaluate the cost and benefit of exploring, visualising, analysing and disseminating information graphically as versus conventional statistical methods.

 Assess the tangible and intangible benefit and cost of visual representation of data.

 Grading of assignments and visual analytics project.

1.3 Business software solution impact

analysis skills YY

 Derive insights and informed decision by performing visual analysis on real world data and describe how these insights impact organisation decision.

2 IT architecture, design and development skills

2.1 System requirements specification

skills

YY

 Identify if a specific requirement is a business requirement or an IT requirement

 Identify if a specific IT requirement is functional or non-functional requirement

 Identify and extract business rules implicitly or explicitly used in existing business processes

 Prepare a system requirements specification report.

2.2 Software and IT architecture

analysis and design skills

YY

 Identify and compare the appropriateness, robustness, and usability of visual analytics techniques, algorithms, software tools, and interface design approaches.

 Prepare technical specifications report.

2.3 Implementation skills

YY  Design and implement a prototype or _{proof-of-concept spatial enabled business}

intelligence application using real world case.

2.4 Technology application skills YY

 Using visual analytics software such as Tableau, Panopticon to gain insights from complex real world datasets.

 Using development software such as Flex Builder to design RIA-based visual analytics applications.

3 Project management skills

3.1 Scope management skills

YY __{Prepare a project implementation plan.}

 Grading of visual analytics project.

3.2 Risks management skills

Y __{Identify key project implementation risk} and suggest possible solutions to minimise the risks identified.

3.3 Project integration and time

management skills

YY __{Design Gantt chart showing project} phases and resource allocations.

 Monitor project implementation using the

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Gantt chart prepared.

3.4 Configuration management skills

Y __{Ability to manage expectation of project} sponsor..

3.5 Quality management skills

Y __{Perform usability study and UAT test.}

 Prepare meta data and application documentation.

4 Learning to learn skills

4.1 Search skills

YY __{Search for case studies, sample} applications and coding examples from unconventional media such as blogs, user forum, face book or wiki.

 Reading and review literature from conventional media such as book, journal and e-media.

 Student’s critics on examples and case studies.

4.2 Skills for developing a

methodology for learning

YY __{Ability to complete assigned taks with}

minimum hand-holding.

5 Collaboration (or team) skills:

5.1 Skills to improve the effectiveness of group processes and work products

YY

 Ability to resolve conflicts while working on the visual analytics project.

 Grading of visual analytics project report.

6 Change management skills for _{enterprise systems}

6.1 Skills to diagnose business

changes Y

 Introduce and design applications that improve the effectiveness or efficiency of current process.

 Grading of visual analytics project report.

6.2 Skills to implement and sustain

business changes N

7 Skills for working across countries, cultures and borders

7.1 Cross-national awareness skills N

7.2 Business across countries

facilitation skills N

8 Communication skills

8.1 Presentation skills Y

 Ability to give a technical presentation.

 Ability to articulate technical findings using managerial communication.

 Prepare project postal highlighting the main features/findings of the visual analytics project.

 Peer evaluation.

 Faculty evaluation.

 Industry presentation

8.2 Writing skills Y

 Prepare industry standard project proposal, technical specification report and user guide.

 Grade and give feedback to assignment 3

Y

This sub-skill is covered partially by the course

YY

This sub-skill is a main focus for this course