Applied Business Analytics and Data Visualisation
Duration
- One Semester or equivalent
Contact hours
- 36 Hours
On-campus unit delivery combines face-to-face and digital learning.
Prerequisites
INF60007 Business Information Systems AND Admission into MA-IT1 - Master of Information Technology
or
INF60012 Cloud Enterprise Systems and Analytics AND Admission into MA-ITPC1 - Master of Information Technology (Professional Computing)
or
INF70008 Business Analytics and Visualisation AND Admission into MA-BIS - Master of Business Information Systems
Aims and objectives
This unit covers issues relating to deriving business insights and foresights from various data sources for decision making purposes. Students gather skills to drive improved decision making process for senior management based on understanding data. Students will know the importance of understanding the short and long term goals of the organisation and how these impact the decision making process. Student will use tools that enable data analysis, Predictive Analytics, and data visualisation to assist in the delivery of delivering real-time actionable intelligence.
Students who successfully complete this unit will be able to:
1 Demonstrate the strategic value of information, data management, and Business Analytics to organisational decision-making activities
2 Apply problem solving and decision-making techniques in order to evaluate the requirements for information and data in business context
3 Critically analyse various datasets using complementary data analytics/business analytics tools to generate insights and/or foresights to approach the organisation's complex problems
Unit information in detail
- Teaching methods, assessment and content.
Teaching methods
Face to Face Mode:
Lectures (12 x 2 hours), Labs (12 x 1 hour)
Independent Learning
114 hours (12 x 9.5 hours)
Student workload:
This includes all:
• Scheduled teaching and learning events and activities (contact hours timetabled in a face-to-face teaching space) and scheduled online learning events (contact hours scheduled in an online teaching space), and
• Non-scheduled learning events and activities (including directed online learning activities, assessments, independent study, student group meetings, and research.
To be successful, students should:
• Attend and engage in all scheduled classes (face to face or online)
• Start assessment tasks well ahead of the due date, and submit assessments promptly
• Read / listen to all feedback carefully, and consider it for future assessment
• Engage with fellow students and teaching staff (don’t hesitate to ask questions)
Assessment
Project and Presentation (Group) 30-50%
Report (Individual) 30-40%
Test (Individual) 20-30%
Content
• Concept of business value from corporate data, the exploitation of information for advantage, types and sources of information value
• Nature and value of business intelligence, business analytics, and how different types of analytics can add value to corporate data sources
• Enterprise data life cycle and data governance
• Knowledge discovery, data mining, data warehousing, data lake
• Business analytics, Online Analytical Processing (OLAP) analysis, metadata.
• Data visualisation, visualisation techniques, dashboard
• The relationship between corporate strategy and Information Systems (IS) strategy pertaining data/data management
• Privacy, ethical, legal issues associated with organisational data
• Cloud based data management and analytics
• Nature and value of business intelligence, business analytics, and how different types of analytics can add value to corporate data sources
• Enterprise data life cycle and data governance
• Knowledge discovery, data mining, data warehousing, data lake
• Business analytics, Online Analytical Processing (OLAP) analysis, metadata.
• Data visualisation, visualisation techniques, dashboard
• The relationship between corporate strategy and Information Systems (IS) strategy pertaining data/data management
• Privacy, ethical, legal issues associated with organisational data
• Cloud based data management and analytics
Study resources
- Reading materials.
Reading materials
Students are advised to check the unit outline in the relevant teaching period for appropriate textbooks and further reading