Applied Statistical Modelling & Health Informatics at King's College London, University of London - UCAS

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Course summary

This course has been created to deliver a skill set and knowledge base in “multimodal” and “big data” analysis techniques, which are a recognised scarcity within UK Life sciences. You will receive world-class training in core statistical, machine learning and computational methodology, and you will have the opportunity to apply your skills to real-life settings facilitated by the world-leading Institute of Psychiatry, Psychology & Neuroscience. The course will apply to a broad spectrum of graduates preparing for a career in medical statistics and health informatics, or professional methodologists and clinical researchers working in the private or public health sector. This course is also suitable if you are a graduate in/work in the fields of computer science, maths, physics, engineering and natural science, including psychology and medicine. The course will prepare participants for the ever-growing need for a sound scientific approach to processing information and generating knowledge in modern health services. Practical skills will be taught through applications to real-life settings in a world-leading research institution in mental health


Entry requirements

A bachelor’s degree with 2:1 honours in Computer Science, Mathematics, Statistics, Physics, Natural Sciences, Electronic Engineering, Psychology or Geographic Information Systems. In order to meet the academic entry requirements for this programme you should have a minimum 2:1 undergraduate degree with a final mark of at least 60% or above in the UK marking scheme. If you are still studying you should be achieving an average of at least 60% or above in the UK marking scheme.


Fees and funding

Tuition fees

No fee information has been provided for this course

Additional fee information

No additional fees or cost information has been supplied for this course, please contact the provider directly.
Applied Statistical Modelling & Health Informatics at King's College London, University of London - UCAS