MSc Financial Engineering

TDevelop your skills to engineer the financial world.

TThe financial sector is a dynamic industry involving much uncertainty and requires proper analytical and practical knowledge for efficient productivity. This Master degree is intended to provide students with the necessary understanding in financial data analysis, risk management, quantitative research, forecasting and efficient decision making, and financial information technology.
Students are expected to acquire considerable knowledge of the main numerical techniques and optimisation models used in quantitative finance. Furthermore, they are expected to develop an understanding of modern econometric techniques, financial risk management tools, derivatives pricing, hedging methodologies and computer tools used in the sector.

Career Prospects

  • Graduates may amongst others join insurance companies, banks, stock exchanges and investment firms.


Considerable knowledge ofthe main numerical techniques used in quantitative finance;
An understanding of modern econometric techniques used in the analysis of financial time series;
An understanding of risk management tools and derivatives pricing and hedging methodologies, used in the financial sector;
An understanding of the principal stochastic differential equations that are used in derivative modelling
and other areas of quantitative finance;
Knowledge to apply optimization models, methods and software to solve problems in computational finance efficiently and accurately;
Knowledge of computer packages for financial applications


  • A bachelor degree with significant content of Mathematics / Statistics.
  • For instance, a bachelor degree in Computer Science, Engineering, Accounting, Economics, Finance or a closely related field, or other qualifications (academic or professional) acceptable to the University of Technology, Mauritius can be considered


13 Lessons1 / 1.5 Years


Supervised Machine Learning Techniques
Data Processing Methods
Essential Mathematics for Machine Learning
Research Methods for Computing
Artificial Neural Networks
Ethics of Artificial Intelligence & Machine Learning
Unsupervised Machine Learning Techniques
Big Data Analytics
Deep Learning Techniques
Smart Autonomous Systems
Reinforcement Learning
Enterprise AI Solutions