Data Science and Analytics for Health MRes

You will study 180 credits in total during your full-time Data Science and Analytics for Health MRes. These are the modules relating to this programme of study for academic year 2020/21. If you are starting in September 2021, these will give you a flavour of the modules you are likely to study. All modules are subject to change. 

Compulsory modules

Machine Learning – 15 credits 

On completion of this module, you should be able to list the principal algorithms used in machine learning, and derive their update rules; appreciate the capabilities and limitations of current approaches; evaluate the performance of machine learning algorithms; use existing implementation(s) of machine learning algorithms to explore data sets and build models.

Artificial Intelligence  - 15 credits 

The module introduces the field of Artificial Intelligence (AI), taking a strongly integrative and state of the art approach based on deep neural networks. In line with the use of AI in key sectors (e.g. finance, health, law), there is an emphasis on the combination of multiple input modalities – specifically, combining images, text and structured data. You’ll gain hands-on experience in developing AI systems to address real-world problems, providing the knowledge and skills necessary to develop an AI system as part of an MSc project.

Data Science and Analytics for Causal Inference and Prediction – 15 credits

The module is designed to give you a comprehensive introduction to linear modelling and equip them with the skills and knowledge necessary to analyse various outcome data types. By the end of the module you will be able to identify suitable linear models for analysing a variety for different outcome types; fit a linear model using statistical software including selection of model parameters; compare between models and assess the appropriateness or otherwise of the fitted model.

Principles of Data Science and Analytics – 15 credits

The module is designed to provide you with a thorough grounding in the principals of planning, conducting, and critically reviewing data scientific research in applied contexts. By the end of the module, you will be confident with: the language and conventions of data science, calculating and interpreting measures of occurrence and association, designing and evaluating scientific studies in populations, identifying and appraising sources of bias, and using causal diagrams to support causal reasoning.

Choose between

Workplace-based Data Science and Analytics Research and Development Project (Long Form)  – 120 credits

The module is designed to support the development of independent and team science practical competencies in applied health data science research and innovation, within the context of real-world systems, challenges and opportunities. The long-form module will include dedicated opportunities to apply and evidence programming competencies as applied to research software engineering drawing upon previous training, expertise and/or experience.

OR

Workplace-based Data Science and Analytics Research and Development Project (Short Form) – 105 credits

The module is designed to support the development of independent and team science practical competencies in applied health data science research and innovation, within the context of real-world systems, challenges and opportunities. The short-form module will build upon the opportunities provided within the Programming for Data Science Module (COMP5712M – a pre-requisite for you attending the short-form of the workplace-based research and development project module) to develop the programming competencies relevant for research software engineering within applied data science contexts.

AND

Programming for Data Science – 15 credits

This module is designed to give those with little or no programming experience a firm foundation in programming for data analysis and AI systems, recognising a diversity of backgrounds. The module will also fully stretch those with substantial prior programming experience (e.g. computer scientists) to extend their programming and system-building knowledge through self-learning supported by on-line courseware.

The full list of module information can be read in the course catalogue.