BUS304 Superannuation and Financial Planning

Unit outline

Important Update: Our aim is to provide you with an optimal learning experience, regardless of how this unit is delivered. Teaching will be delivered in line with the most current COVID Safe health guidelines.  This may include a mix of online and face-to-face.  Please check the learning management system for announcements and updates. Thank you for your flexibility and commitment to studying with Sydney Institute of Higher Education. 
Enrolment modes: Year 3, Semester 1
Credit point(s): 12.5
EFTSL value: 0.125
Prerequisite: Nil
Typical study commitment: Students will on average spend 10 hours per week over the teaching period undertaking the teaching, learning and assessment activities for this unit.
Scheduled learning activities: 4 timetabled hours per week, 6 personal study hours per week

Unit description 

This unit will introduce students to the foundations of superannuation and family planning. It will examine the significant role of government’s policy towards superannuation and the historical, international and legal context of this policy. Students will cover critical topics in superannuation including self-managed superannuation funds, contributions, regulations, financial planning and investments. 

Unit Outline Outcomes (ULO)

  On the successful completion of this units student will be able to:  
ULO1 Understand the importance and value of superannuation and financial planning.  
ULO2 Identify various types of superannuation funding provided by the Australian government.  
ULO3 Demonstrate an understanding of retirement income policy and the complexities of the superannuation system.  
ULO4 Verify suitable superannuation strategies to suit an individual client’ requirement.  
ULO5 Analyse superannuation structures and strategies for different scenarios.  

Topics to be included  

1. Data mining and exploratory data analysis
2. Data classifications and clustering techniques
3. Big data analytics
4. Univariate and multivariate statistical analysis
5. Neural networks
6. Bayesian networks
7. Sparse linear methods
8. Social media analytics, methods, processes, applications and ethics 
9. Location intelligence
10. Time series forecasting
11. Data analytic decision models
12. Model evaluation techniques


Assessment Description Grading and weighting
(% total mark for unit)
Indicative due week 
Assessment 1: Mid-Semester Exam 30% Mid-semester exam week 
Assessment 2: Group Assignment 30% 10
Assessment 3: Final Exam 40% Final exam week

The assessment due weeks provided may change. Your lecturer will clarify the exact assessment requirements, including the due date, at the start of the teaching period.