Currently offered across four of our MSc programmes, the Data Science stream prepares engineers to lead in an increasingly data-driven world. From statistical modelling and machine learning to data engineering and real-world coding, this stream equips engineers with the in-demand skills needed to solve future Civil and Environmental Engineering challenges.
Students will master causality and inference for data interpretation, build and deploy AI-driven models, and work with cloud platforms like AWS and Azure. Hands-on experience with Python, R, and MATLAB ensures graduates are ready to tackle live industry challenges.
The Data Science stream is currently offered with the following MSc programmes:
What you'll study
In this video, Dr Truong Le explains how our Data Science stream equips students with cutting-edge skills in statistical modelling, machine learning, and data engineering — preparing them for careers both within and beyond Civil and Environmental Engineering.
Modules in this stream
Take a look below for further information about the four modules that make up this programme.
What you'll study
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.
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.
This module will provide you with practical experience in constructing and validating models and algorithms and the necessary advanced computational skills to enable you to apply this knowledge to solving a range of complex civil engineering problems. The module includes: (1) construction, implementation, and validation of machine learning algorithms; (2) deployment of advanced machine learning libraries to solve complex problems from a broad range of civil engineering applications; (3) coverage of different machine learning approaches; (4) the operation of developer environments; (5) database development; and (6) advanced data manipulation and data visualization techniques. The module will be taught using the Python programming language and will build upon a broad range of established modelling libraries.
Students will complete a Design Project for the Data Science Stream and aligned with their chosen MSc specialism.
This module will provide you with practical experience in constructing and validating models and algorithms and the necessary advanced computational skills to enable you to apply this knowledge to solving a range of complex civil engineering problems. The module includes: (1) construction, implementation, and validation of machine learning algorithms; (2) deployment of advanced machine learning libraries to solve complex problems from a broad range of civil engineering applications; (3) coverage of different machine learning approaches; (4) the operation of developer environments; (5) database development; and (6) advanced data manipulation and data visualization techniques. The module will be taught using the Python programming language and will build upon a broad range of established modelling libraries.