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Dissertation
Dissertation in Health Data Science: The dissertation is an independent research project which is researched and written under the supervision of a member of academic staff. The model for the dissertation is a journal article.
Compulsory modules
[Module suspended for 2020-21] Principles of Epidemiology Applied to Electronic Health Records Research:The module provides core epidemiological skills enabling students to interpret electronic health records (EHR) research and to design analyses using EHR.
Data Methods for Health Research: This module will deal with contemporary computational tools and approaches for managing health and biomedical data in the context of research.
Basic Statistics for Medical Sciences: This module aims to introduce the basic statistical methods commonly used in biomedical, epidemiological, and public health research, and to learn to make practical use of the statistical computer package STATA.
Regression Modelling: this is a course in biomedical statistics and statistical computing which will provide you with an advanced level of knowledge in key concepts, and introduce you to regression modelling.
Principles of Health Data Science: This module introduces students to the main principles of health data science and provides them with an overview of the main research areas where these are applied.
Software Development with Python for Health Data Science: In this course, students with a limited knowledge of programming will learn how to construct reliable, readable, efficient software for data-intensive research in a collaborative environment.
Optional modules
Students choose three of the following:
[Module suspended for 2020-21] Advanced Statistics for Records Research: The module aims to further students’ understanding of statistical methods used on records data and introduce them to a number of specific techniques for dealing with the challenges of interpreting and drawing inferences from electronic healthcare records.
Machine Learning in Healthcare and Biomedicine: The module provides an introduction into the principles of machine learning in healthcare and biomedicine, covering the key concepts involved in designing and evaluating approaches to machine learning.
Essentials of Informatics for Healthcare Systems: This module provides a foundation for understanding health informatics in the wider health landscape and how it impacts on the delivery of healthcare.
Public Health Data Science: This module aims to give health data analytics students an introduction to the core themes of public health and how data science can be used to promote, protect health and well-being, prevent ill-health and prolonging life through the organised efforts of society.
Computational Genetics for Healthcare: This is an intensive practical course for anyone wanting to have hands-on experience of application of the latest methodology within human genetics.
Advanced Statistical Analysis: The course will cover a range of more advanced statistical techniques used in healthcare.
Advanced Machine Learning for Healthcare: In this module, students with basic knowledge of machine learning (ML) will learn in-depth ML algorithms and data analysis focusing on recent techniques, such as deep learning with neural networks.
