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Data Analytics in Insurance 1 (ACTL90023)
Graduate courseworkPoints: 12.5On Campus (Parkville)
Overview
Availability | Semester 1 |
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Fees | Look up fees |
This subject aims to provide students with basic training on modern data analytics methods, which include linear regression, classification, resampling methods, spline-based methods, generalised additive models and support vector machines. This subject focuses on applying the above methods to modelling non-life insurance claims frequency and severity.
Intended learning outcomes
Intended learning outcomes
- Recognise major types of non-life insurance data and their main characteristics.
- Demonstrate a depth of knowledge in linear regression methods, regression splines and smoothing splines.
- Demonstrate basic understanding in various statistical learning models that include classification methods, resampling methods, generalised additive models and support vector machines.
- Use computer software R to apply various statistical learning models for insurance related applications.
- Apply basic linear model selection and regularisation techniques when conducting linear regression analyses.
- Interpret the results of data analytics conducted on real insurance data
- Compare benefits/drawbacks of competing models and methods, relevant to real problems.
Generic skills
- High level of development
- Written communication
- Logical problem solving
- Statistical reasoning
- Application of theory to practice
- Interpretation and analysis
- Synthesis of data and other information
- Evaluation of data and other information
- Use of computer software
Last updated: 8 November 2024