Data Science and Analytics for Health MRes

The following modules are available in 2023/24 for your full-time Data Science and Analytics for Health MRes and are examples of the modules you are likely to study. All modules are subject to change. You will study 180 credits in total.

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.

Data Science – 15 credits

The aim of the module is for you to understand methods of analysis that allow people to gain insights from complex data. The module covers the theoretical basis of a variety of approaches, placed into a practical context using different application domains.

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

The aim of this module will be 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.

Programming for Data Science – 15 credits

The module introduces the fundamental skills of programming and software system development. It aims to give students the skills and experience to produce simple computer-based applications for a range of sectors. It prepares students to develop and integrate systems using Artificial Intelligence and Data Analytics techniques.

Optional modules:

Deep Learning – 15 credits

The module introduces the field of Deep Learning, taking a strongly integrative and state of the art approach. 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. 

Data Mining and Text Analytics – 15 credits

Introduction to linguistic theory and terminology. Understand and use algorithms and resources for implementing and evaluating text mining and analytics systems. Develop solutions using open-source and commercial toolkits. Consider the applications of data mining and text analytics through case studies in information retrieval and extraction.

Business Analytics and Decision Science – 15 credits

This module aims to introduce you to key concepts in business analytics, with a special emphasis on common areas of application. It also explores the links between the behavioural (decision science) perspective on decision support and the management science/business analytics perspective.

Innovation Management in Practice – 15 credits

The module aims to extend your knowledge of the concepts and principles studied in other modules in innovation and provide critical understanding of specific innovation principles, practices, tools and processes needed for the management and development of innovation.

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