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. |
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| 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
| 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.