Health Data in Practice (15 credits)
The module provides an introduction to health data in practice with a focus on health care delivery challenges and patient and population health outcomes from an interdisciplinary perspective. It will provide students with a grounding in legal and ethical frameworks governing health data access and use, and the role of patient, health professional and public engagement for delivering the full potential of health data sciences for public benefit.
Assessment: 2 written assessments: 20%:80% weighting
Effective and Efficient Evaluation (15 credits)
Novel designs to maximise the use of health data to provide meaningful answers to important health and health service-related questions relevant to the effectiveness, cost-effectiveness and safety of interventions, including designs to evaluate data-driven technologies, and exploration of novel approaches to defining minimally sufficient data for evaluation.
Assessment: 2 coursework assignments (50%; 30% weighting); presentation (20% weighting).
Qualitative Methods for Health Research (15 credits)
This module will introduce learners to the principles of interpretive research and to a broad range of qualitative research practice including: interviews; focus groups; ethnographic approaches; participatory research methods; qualitative synthesis; mixed- methods designs. The importance of integrating theory and ensuring ethical practice in the design, conduct and analysis of research will be emphasised throughout. The module will lead learners through the research cycle from formulation of research idea to ensuring research impact with a focus on learning-by-doing and improving reflective practice.
Assessment: 2 coursework assignments (20%; 80% weighting)
Epidemiology and statistics for health data scientists (15 credits)
This module will provide an introduction to epidemiology and statistics with a focus on quantitative analytic approaches. Develop the knowledge and understanding necessary to design, analyse and interpret epidemiological studies with an application to public health and clinical practice. Gain practical skills in using statistical software to clean data, perform statistical analyses and display data. Learn to interpret findings crucial for evidence-based healthcare research, critically evaluate research and contribute to advancements in public health. This module equips students with the expertise to tackle complex health challenges through advanced epidemiological approaches and statistical modelling.
Assessment: 40% Skills for clinical practice, public health, or policy; 10% Critical appraisal; 50% Examination
Credit value: 60 credits
The modules provide the opportunity to complete a substantial research project with a research group, focusing on one of the four themes. You will select from a range of projects. On completion of the projects, you will be able to:
- carry out background research into a project
- design and implement your own studies
- interpret data and analyse results
- prepare a scientific project report
Assessment: 100 per cent coursework.
Credit value: 15 each
Design for Human Interaction
This research-led course introduces psychological theories of human communication to understand how technology can enrich and transform human interaction. It introduces the tools and techniques necessary for a principled approach to the design and evaluation of such technology.
Assessment: 80% examination, 20% coursework.
Interactive System Design
The main areas of study are (i) interaction and design (ii) modelling of interaction (iii) the design process (iv) design principles and (v) usability evaluation. Various types of interfaces are considered including those encountered on the web and mobile computing devices. A historical perspective is encouraged in order to provide a means of understanding current and projected developments in the discipline and profession of interactive computer system design. The module will include seminars and group laboratory classes in which analysis, design and evaluation methods will be used in practical contexts.
Assessment: 60% examination, 40% coursework.
Natural Language Processing
Natural Language Processing (aka Computational Linguistics) has become an important and growing field in the last decade. Many of the most important applications for computing now involve the processing and understanding of spoken or written language: machine translation, question answering, news summarisation, text and opinion mining, and spoken dialogue systems like the iPhone's Siri. This module will introduce the core techniques in language processing, including statistical and rule-based approaches, and show how to apply them to the main application areas.
Assessment: 100% coursework
Neural Networks and NLP
Natural Language Processing (NLP) has become one of the most important technologies in Artificial Intelligence. Automatic methods for processing natural language now find application in almost every aspect of our communication in person or online, in particular through social media. The increased use of Neural Networks (NN) has played an important role in the most recent progress of NLP, as NN techniques have delivered improved performance in applications ranging from language modelling (next word prediction) to speech to machine translation to sentiment analysis. The proposed module introduces you to this cutting-edge approach to developing NLP systems.
Assessment: 60% examination, 40% coursework.
Applied Statistics
The module introduces core statistical concepts for practical data analysis. It will provide students with the skills to model data sources, analyze their statistical properties, visualize them in different ways and fit the samples to a known probabilistic model.
Assessment: examination 60%; 4 coursework assignments 10%:10%10%:10% weighting
Risk and Decision-making for Data Science and AI
This module provides a comprehensive overview of the challenges of risk assessment, prediction and decision-making covering public health and medicine, the law, government strategy, transport safety and consumer protection. Students will learn how to see through much of the confusion spoken about risk in public discourse, and will be provided with methods and tools for improved risk assessment that can be directly applied for personal, group, and strategic decision-making. The module also directly addresses the limitations of big data and machine learning for solving decision and risk problems.
Assessment: Examination 50% weighting; 2 assessed coursework assignments 25% weighting each
Data Mining
Data that has relevance for decision-making is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations such as the Internet, e-commerce, electronic banking, point-of-sale devices, bar-code readers, and electronic patient records. Data mining is a rapidly growing field that is concerned with developing techniques to assist decision-makers to make intelligent use of these repositories. The field of data mining has evolved from the disciplines of statistics and artificial intelligence.
This module will combine practical exploration of data mining techniques with a exploration of algorithms, including their limitations. Students taking this module should have an elementary understanding of probability concepts and some experience of programming.
Assessment: 60.00% examination; 4 assessed coursework assignments 10% weighting each
Machine Learning
The aim of the module is to give students an understanding of machine learning methods, including pattern recognition, clustering and neural networks, and to allow them to apply such methods in a range of areas.
Assessment: 60.00% examination; 2 assessed coursework assignments 20% weighting each
Health Inequalities and Social Determinants of Health
This module will examine the theories and evidence underpinning social inequalities in health (defined as the unfair and avoidable differences in health status). It will consider structural/material and psychosocial theories, and hypothesis about social drift, self-selection, and genetics. Attention is given to the WHO Commission on Social Determinants of Health. Sources of data and measurement of scale of inequalities between and within groups are addressed. The module will consider association with income and distribution of money, resources, and power at global, national, and local level. Policy interventions and their different approaches will be explored including universal and targeted or selective approaches to reducing inequalities by reducing the inequitable distribution of power, money, and resources.
Assessment: 100% written coursework
Health Economic Analyses
The module will introduce learners to key microeconomic concepts and principles, their relevance to the health economy and the need for alternative approaches to priority setting and resource allocation. The module will then discuss key health economic analytical frameworks to inform resource allocation in health, exploring methods of economic evaluation, health policy evaluation, economic analysis of public health interventions, and analysis of inequalities in health and health care. Throughout the module, the focus will be on developing learners¿ ability to critically appraise, conceptualize, design, and carry out appropriate health economic analyses.
Assessment: 40% Project Report (1500 words); 60% Written Examination (2 hours)
Health Systems Theory, Policy and Political Economy
In this module we examine trends towards the reform of health systems in the context of globalisation. Particular attention is given to the impact of neoliberal policy and commercialisation; the move towards universal health coverage; policy on integration; and decentralisation. The role of actors in shaping policy will also be covered, as well as the impact of trade and investment related agreements on health systems. The impact of other aspects of globalisation on health systems - such as migration - will also be covered.
Assessment: 20% Group Presentation (20 mins); 80% Essay (2000 words)
