Real world health data often has complex structures, which can impact on individual health outcomes. Outcomes may depend on existing administrative or geographic structures, or the risk of developing disease may be based on complicated combinations of underlying factors. With growing access to high-dimensional datasets, both in health research, and across the data-science spectrum, suitable statistical methods are essential to harness the information within these datasets. This module will provide an understanding of how appropriate statistical methods can be selected, and teach the skills necessary to conduct analyses on real world data. In this module, students will learn a variety of statistical modelling and learning algorithms, and be introduced to state-of-the-art machine learning methods for data mining and classification. The tools developed during this module will provide students with the understanding and skills needed to perform complex analysis as part of a data science team in their future careers. Students will benefit from research-connected teaching with academic staff, and develop hands-on experience of data analysis in data-labs.