Actuarial Analytics and Data I (ACTL30008)

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.