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Wolfson Institute of Population Health

MRes Health Data in Practice

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

Introduction to Social Science 1: Epistemology, Research Design, and Qualitative Methods (30 credits) 

The module provides you with advanced research skills, including the ability to select and use relevant resources effectively and to devise research questions appropriate for postgraduate research. You will develop the capacity to undertake independent guided research at postgraduate level. 

Assessment: 2 written assessments: 40%:60% weighting 

Introduction to Social Science 2: Quantitative Methods and Data (30 credits)

This module teaches you to use advanced quantitative skills appropriate for postgraduate research. Further, you will be able to analyse, interpret, critique and replicate published research using quantitative research methods and will acquire sufficient technical competence using SPSS to perform a range of quantitative techniques in your own research.

Assessment: Examination 60.00% weighting; assessed coursework 40.00% weighting

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.

Effective and efficient evaluation

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: 100% 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


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