The aim of this add-on option of our degree is to provide a concise and comprehensive introduction to key scientific methods for data analysis and mathematical optimisation from both a theoretical and an applied viewpoint.

This add-on is made up of the following modules:

Statistical Modelling
This module will provide you with a comprehensive understanding of statistical modelling fundamentals from a theoretical and applied viewpoint. It will develop the relevant theory, methodology and computational techniques required for you to formulate and implement statistical models to represent real-world phenomena. The course will also teach you how to program statistical models in the R programming language using both R and the RStudio graphical user interface (GUI). A pre-requisite for this course is that you must have a sufficient background in mathematics, including algebra, matrix algebra, and multivariate calculus.

Machine Learning
This module will provide you with a comprehensive understanding of machine learning concepts and their application to civil engineering applications. It will cover the three principal subfields of modern machine learning, namely supervised, unsupervised and reinforcement learning. Application examples will be drawn from a broad range of civil engineering applications, including transport. The module will also teach you how to implement machine learning models using the Python programming language, using common numerical analysis libraries (such as NumPy), and specialised tools, such as scikit-learn and PyTorch.

Mathematical Optimisation
This module will provide you with the mathematical and computational concepts required for formulating and solving a wide range of mathematical optimisation problems. The module will cover the mathematical definitions of objective functions, constraints, optimality conditions and optimal solutions. You will be exposed to different optimisation problem formulations, solution methods, and examples from different application domains across civil engineering (including transport).    

These modules are available to those on the following programmes:

MSc Transport with Data Science (1YFT)

MSc Environmental Engineering with Data Science (1YFT)

MSc Geotechnical Engineering with Data Science (1YFT/2YPT)