MSc Actuarial Science with Business Analytics

This module provides a collection of standard analytical methods and explains how data analysis is performed in the real world. It represents an introduction to specific tasks that a business analyst encounters on a daily basis that ultimately help in analysing, communicating and validating recommendations to change the business and policies of an organisation. Furthermore, the module provides the foundation for using R to translate theory into practice.

You will learn how to apply compound interest theory to find the present value or the accumulation of a cash flow, and to apply financial mathematics to solve a wide range of practical problems also via computer-based applications. You will analyse and compare alternative capital projects and perform investment valuation.

This module provides an overview of machine learning concepts, techniques and algorithms used in practice to describe and analyse complex data and design predictive analytics methods. You should expect to engage with the main idea and intuition behind modern machine learning tools from a practical perspective. Standard computing skills in R, Python and Matlab will be used to put in practice the theory discussed in the lectures.

You will gain an understanding of a broad range of life insurance products and their pricing, and a mastery of life insurance mathematics. You will be able to calculate gross premiums and reserves for fixed and variable benefit contracts. You will be introduced to annuities and assurances involving two lives, and multiple decrement models. Finally, you will be able to apply mathematics and statistics to related practical problems via computer-based applications.

Students have the opportunity to study optional modules in this year.

This module is designed to provide a fundamental understanding of R programming and no previous programming experience is expected. The teaching model is learning by doing and basic concepts are built up in an incremental manner. The teaching material is formulated via multiple R code examples that enable the students to work independently when dealing with small R programming tasks.

This module is designed to provide a fundamental understanding of Python programming and no previous programming experience is expected. The teaching model is learning by doing and basic concepts are built up in an incremental manner. The teaching material is formulated via multiple Python code examples that enable the students to work independently when dealing with small Python programming tasks.