Financial Management for Engineers (MSIN0171)

The module covers key elements of Financial Management and associated quantitative and qualitative aspects. These are essential ingredients of the skills and knowledge portfolio for aspiring senior managers. It provides students with a conceptual and theoretical basis for managing finance in the context of making financial decisions. It explores and evaluates measurement and valuation matters relating to short and longer term financial operational decision making, issues in financing and investment decisions, and policies for dividend and returns of value to owners. Key aspects explored include financing decisions, cost of capital issues, the theory and application of risk-related capital investment decisions, the nature of shareholder value, and company valuation. Accordingly, this module identifies and evaluates the key financial challenges facing directors and managers concerning the selection of projects to invest in, the financing of such investments, and issues relating to the management of the relationship with finance providers.

This module provides a general introduction to programming in Python using Jupyter Notebooks and then moves on to an exploration of key Python packages for...

Programming for Business Analytics (MSIN0143)

This module provides a general introduction to programming in Python using Jupyter Notebooks and then moves on to an exploration of key Python packages for undertaking analytical projects such as MatPlotLib, NumPy and Pandas.


The module covers the preparation of datasets and introduces students to simple examples of machine learning algorithms. The importance of building insightful visualisations will also be emphasised.

This module teaches the fundamentals of probability theory, which are essential for future management scientists. Students will be introduced to topics such as...

Mathematics III (Probability Theory) (MSIN0022)

This module teaches the fundamentals of probability theory, which are essential for future management scientists. Students will be introduced to topics such as joint distributions of random variables, properties of expectations, basic limit theorems, in addition to the basics of Bayesian and decision analysis. 

The module includes examples that illustrate the concepts and their practical application in management.