Actuarial Analytics and Data I (ACTL30008)

Undergraduate level 3Points: 12.5On Campus (Parkville)

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Overview

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Semester 1
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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

On completion of this subject, students should be able to:

  • 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, re-sampling methods, generalised additive models and support vector machines.
  • Use relevant computer software 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: 4 March 2025