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Actuarial Analytics and Data II (ACTL40012)
HonoursPoints: 12.5On Campus (Parkville)
About this subject
- Overview
- Eligibility and requirements
- Assessment
- Dates and times
- Further information
- Timetable (login required)(opens in new window)
Contact information
Semester 2
Genevieve Hayes: genevieve.hayes@unimelb.edu.au
Overview
Availability | Semester 2 |
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Fees | Look up fees |
This subject aims to further develop students’ knowledge of modern analytical tools and techniques, including GLM, shrinkage techniques (e.g., LASSO and ridge regression), tree-based methods (e.g., random forests and GBM) and neural networks. It also teaches students to connect data analytics work to the actuarial control cycle and real-world business environments. Effective communication of findings to a range of business decision making audiences is also stressed.
Intended learning outcomes
On successful completion of this subject, students should be able to:
- Explain where and how their data analytics work can add value to the business environment and strategy
- Source, interpret, evaluate and prepare data for modelling
- Use judgment to select appropriate predictive analytic techniques for a given business problem
- Apply predictive analytic techniques to solve estimation and classification problems
- Evaluate and compare performance of different models
- Communicate findings to a range of audiences
Generic skills
- Written communication
- Problem solving
- Statistical reasoning
- Application of theory to practice
- Predictive analytic
- Interpretation and analysis.
Last updated: 31 January 2024