Skip to main content

New data science units for 2023/24

Read unit details for units in our MSc Mathematics with Data Science for Industry and MSc Data Science and Statistics (Health) courses starting in 23/24 onward.

Applied machine learning

Credits: 6 (equivalent to 12 CATS credits)

Gain a general foundation in machine learning, covering a range of methodologies for supervised and unsupervised learning. By the end of the unit, you should be able to critically analyse and implement machine learning algorithms, apply them to real-world data, evaluate their performance, and write technical reports to summarise your findings.

Programming for data science

Credits: 12 (equivalent to 24 CATS credits)

Develop your skills in numerical programming for data science and machine learning applications. After completing this unit you will be able to implement and critically evaluate low-level data science functionality from scratch, and complex methodologies using relevant libraries. You will gain experience in methods and structures to help you handle, process and analyse large datasets, and have an understanding of sustainable software development.

Data science and statistics for health

Credits: 12 (equivalent to 24 CATS credits)

Learn the concepts and methods of data science and statistics relevant to applications in the health sector, such as data sources, data management, statistical analysis, interpretation and reporting. After completing the unit, you will have extensive experience in the processes involved in initial data handling, preparation and assessment, as well as an understanding of how to handle, manage and analyse health data in the context of legal, ethical and professional considerations. This unit runs across both semester 1 and semester 2.

Mathematics of machine learning

Credits: 6 (equivalent to 12 CATS credits)

Develop a deeper understanding of modern machine learning, focusing on the underlying mathematics and numerical realisation of neural networks. By the end of the unit, you should be able to critically analyse the mathematical formulation of deep learning algorithms, implement them computationally and appreciate the importance of algorithm efficiency.

On this page