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  3. Data Analytics in Insurance 2

Data Analytics in Insurance 2 (ACTL90019)

Graduate courseworkPoints: 12.5On Campus (Parkville)

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Overview

Availability
Semester 2
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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 analytics
  • Interpretation and analysis

Last updated: 8 November 2024