Health Data Analytics and Machine Learning - MSc

Our MSc in Health Data Analytics and Machine Learning is a one-year full-time course aimed at building a solid and common background in analysing health data.

Your main objective is to develop skills in using appropriate cutting edge quantitative methods to fully exploit complex and high dimensional data.

The course is delivered in collaboration with the Data Science Institute, with teaching from both the School and Institute undertaken by international experts with strong methodological background and expertise in the application of these approaches to large-scale medical and clinical data. The programme features extensive project-based learning using real data sets and addressing real scientific questions through module-specific projects work, and individual research projects.

This Master’s is integrated in the research priorities of the School of Public Health, the Data Science Institute, the MRC Centre for Environment and Health, the UK Dementia Research Institute, and the pan-London Health Data Research UK initiative, through:

• the contribution to teaching of key staff members (lectures, seminars, journal clubs)
• the definition of research projects stemming from data available and yet under-exploited in each institute

As such, not only the programme will equip students with cutting-edge statistical and machine learning techniques that are required to explore emerging ‘Big’ health data, but will also provide extensive experience in their application in a real-life setting in Environmental, Molecular, Cancer, and Computational epidemiology as well as in Population and Health sciences.

Each module and the six-month research project includes project-based work. Projects are based on real data and will address real scientific questions from research staff within School of Public Health, Data Science Institute and industrial partners.

Our MSc in Health Data Analytics and Machine Learning is delivered in partnership with the Data Science Institute.

Study Programme

The programme is a full-time 12 month taught Master’s course, which runs from October-September.

The course is divided between six core taught modules and one six-month research project.

In term one, you share your first two modules with MSc Epidemiology and Master of Public Health students, ensuring a common foundation in epidemiology. The third core module is specific to this course.

You will also set and agree a research project focus in your first term.

In term two, you turn your focus to statistical methods in the three remaining core modules, as well as continuing in-depth planning for your research project.

Your third term is predominantly made up of the research project.

Careers

Graduates of this course will have acquired the strong methodological background needed to perform in-depth analysis of medical and epidemiological high throughput datasets.

You will graduate prepared to pursue further study at doctoral level, become an expert analyst in industry, and join large data companies.

Structure

Modules shown are for the current academic year and are subject to change depending on your year of entry.

Please note that the curriculum of this programme is currently being reviewed as part of a College-wide process to introduce a standardised modular structure. As a result, the content and assessment structures of this course may change for your year of entry. We therefore recommend that you check this course page before finalising your application and after submitting it as we will aim to update this page as soon as any changes are ratified by the College.

Find out more about the limited circumstances in which we may need to make changes to or in relation to our courses, the type of changes we may make and how we will tell you about changes we have made.

You take all of the core modules below.

• Clinical Data Management
• Computational Epidemiology
• Introduction to Statistical Thinking and Data Analysis
• Machine Learning
• Principles and Methods in Epidemiology
• Research project
• Translational Data Sciences

Teaching and Assessment:

Teaching Methods

• Case studies
• Formal presentations
• Group work exercises
• Lectures
• Seminars and practical coding activities

Assessment Methods

• Individual and group coursework
• Oral presentations
• Research project report
• Written examinations