Applied Business Analytics and Data Visualisation | Units of study... | Swinburne University of Technology

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INF80031 12.5 Credit Points Hawthorn Available to incoming Study Abroad and Exchange students

Duration

One Semester or equivalent

Contact hours

36 hours face to face + blended

On-campus unit delivery combines face-to-face and digital learning.

2023 teaching periods

Hawthorn Higher Ed. Semester 1
Dates: 27 Feb 23 - 28 May 23 Results: 4 Jul 23 Last self enrolment: 12 Mar 23 Census: 31 Mar 23 Last withdraw without fail: 14 Apr 23

Hawthorn

Higher Ed. Semester 1

Dates:
27 Feb 23 - 28 May 23

Results:
4 Jul 23

Last self enrolment:
12 Mar 23

Census:
31 Mar 23

Last withdraw without fail:
14 Apr 23

Prerequisites

INF60007 Business Information Systems AND Admission into MA-IT1 - Master of Information Technology

INF60012 Cloud Enterprise Systems and Analytics AND Admission into MA-ITPC1 - Master of Information Technology (Professional Computing)

INF70008 Business Analytics and Visualisation AND

Admission into MA-BIS - Master of Business Information Systems or Business Analytics Specialisation

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

4 Apply skills to function as an effective member or leader of a team

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:

For all teaching and learning structures (both face to face and online), students are expected to spend an average of 150 hours per unit in total over the duration of the study period.

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:

• Read all prescribed materials and/or view videos in preparation for each class
• 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

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