Master of Data Science (Health)

Entry requirements

A UK first or upper second class honours degree or equivalent in ANY degree that doesn’t include a strong data science component including those in social sciences, the arts and humanities, business, and sciences.

Candidates with health-related and medicine degrees are strongly encouraged to apply.

Evidence of competence in written and spoken English if the applicant’s first language is not English:

• minimum TOEFL requirement is 102 IBT (no element under 23)
• minimum IELTS score is 7.0 overall with no element under 6.0 or equivalent

Months of entry

September

Course content

From personalised medicine, to smart cities and sustainable solutions, data science is building a better world. At the same time, developments in technology have made the field of data science more accessible than ever, creating new opportunities to gain insight into the interactions between people and their environment – nowhere is this more true that in the health sector where the effective use of data is playing a vital role in tailored care for individuals and in improving health outcomes for the public as a whole.

Drawing on this, we have created the Master of Data Science (Health), a conversion course that opens up a future in data science even if your first degree is in a non-quantitative subject. You will learn from practicing researchers who are making a difference across a range of industries. Shared core modules across the suite of MDS courses will equip you with general data science skills and an understanding of how to apply those skills effectively, while subject-specific modules focus on the complex data and specialist methods used in the health system. It is equally suitable whether you are planning to use quantitative analysis in a research capacity, or if you are a health or social care graduate who wants to develop transferable data and modelling analysis skills for the workplace.

The course begins with a range of introductory modules before progressing to more advanced contemporary techniques such as statistical analysis (in R) and computer science (in Python). You will also take modules that explore the use of data for clinical and public health decisions, relevant modelling techniques such as survival and epidemiological methods, and address questions such as governance and privacy.

The course culminates in the research project, an in-depth investigation into an area of interest in which you apply the skills you’ve learned during the course to a specific topic or issue in health or social care.

Course structure

The Data Science Research Project is a substantial piece of research into an unfamiliar area of data science, or in your subject specialisation area with a focus on data science. The project can be practical, theoretical or both, and is designed to develop your research, analysis and report-writing skills.

Critical Perspectives in Data Science develops your understanding of the production, analysis and use of quantified data, and how to analyse these practices anthropologically. You will learn to think ethically and contextually about quantified data, and how to apply this knowledge to practical problems in data science, including your own research project.

Health Informatics and Clinical Intelligence examines the concepts and skills for generating health and medical evidence from electronic medical/health records and health system datasets. You will explore areas such as fundamentals of health informatics; public health data; electronic health/medical records; and applications of health informatics.

Models and Methods for Health Data Science introduces the knowledge and skills for the modelling and analysis of routinely collected health data. It includes areas such as the basics of epidemiology; health economic modelling; modelling techniques for discrete data; and survival analysis.

Introduction to Statistics for Data Science focuses on the fundamentals of statistics you will need for data science. The module covers topics such as exploratory statistics, statistical inference; linear models; classification and clustering methods; and resampling and validation.

The remainder of the course will be made up of core and option modules which will vary depending on prior qualifications and experience. These have previously included:

• Introduction to Computer Science
• Introduction to Mathematics for Data Science
• Programming for Data Science
• Text Mining and Language Analytics
• Data Exploration, Visualisation and Unsupervised Learning
• Strategic Leadership
• Machine Learning
• Computational Social Science
• Society, Health and Wellbeing
• Ethics and Bias in Data Analysis

Fees and funding