Data Analytics in Insurance 1 (ACTL90023)

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