Back to Modules
IS428

Visual Analytics for Business Intelligence

1 CreditsBoth

Description

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.

Requisites

Prerequisites: None

Co-requisites: None

Anti-requisites: None

Attributes

Department: SCIS

Course Level: Undergraduate

Tracks: IS/T4BS: Business Analytics Track

Areas: Advanced Business Technology Major Analytics Major Business Options Business-Oriented Electives Data Science and Analytics Electives Econ Major Rel/Econ Options Grad Req - Dig Tech/Data Ana (Intake 2024 onwards) IS Depth Electives IT Solution Development Electives Smart-City Management & Tech Electives Social Sciences/PLE Major-related Technology & Entrepreneurship

Learning Outcomes

1. Explaining the concepts and principles of Visual Analytics. 2. Describing the differences between Visual Analytics, Data Visualisation, Statistical Graphs 3. Explaining the basic concept of visual variables and applying these concepts and best practice static 4. Explaining interactive techniques and best practice, and applying these techniques in designing interactive data visualisation. 5. Understanding the data characteristics of numerical data and building data visualisation by using appropriate univariate graphical methods. 6. Understanding the characteristics of multivariate data and building data visualisation by using appropriate multivariate visualisation methods. 7. Understanding the characteristics of time-series data and building data visualisation by using appropriate time-series visualisation methods. 8. Understanding the characteristics of geographical data and building data visualisation by using appropriate geo-visualisation methods. 9. Understanding the characteristics of network data and building data visualisation by using appropriate network graph visualisation methods. 10. Explain the concepts and principles of Information Dashboard. 11. Building business dashboard by using Commercial off-the-shelf (COTS) software. 12. Designing visual analytics application programmatically by using free and open source software and packages.

Graduate Learning Outcomes

Disciplinary Knowledge, Multidisciplinary Knowledge, Interdisciplinary Knowledge, Critical thinking & problem solving, Communication, Self-directed learning

Competencies

Data Analytics, Systems Design, User Experience Design, User Interface Design, Data Visualisation

View Source Listing