Data analysis – an enhanced toolkit for Actuaries

Digital disruption has led to the generation of large volumes of data and the challenge of managing and making use of such data globally. The modern tools and techniques in data science help with drawing insights from this data. Analysing data is a core skill for Actuaries but there is an increasing need to become familiar with emerging techniques in this fast moving and digitally transformed environment. This course will cover data visualisation, data manipulation and ethics as well as machine learning and AI. Learning Outcomes - Solve a business problem by supplementing traditional actuarial techniques with new modern analytical techniques. - Source, prepare, manipulate and evaluate data to be used with new modern machine learning methods. - Understand the advantages and limitations of different modern machine learning methods and apply judgement when applying these to solve actuarial problems. - Communicate the implications and limitations of these new methods to non-technical business executives. - Understand professional and ethical considerations when using these methods to perform analytical work in a business environment. Key Topics - Overview of machine learning process in an actuarial business content - Communication using R - Key tools for data exploration, management and manipulation - Key tools for data visualisation and exploratory analysis - Parametric regression methods - Non-parametric regression tree methods - Classification problems - Unsupervised learning Activities - Self directed online resources - Case studies, quizzes and exercises in R - Final report_assessed Volume of Learning 75 hours