Applied Statistical Modelling & Health Informatics MSc Program By King's College London |Top Universities

Applied Statistical Modelling & Health Informatics MSc

Applied Statistical Modelling & Health Informatics MSc

King's College London

Denmark Hill Campus, London, United Kingdom
  • QS World University Rankings
    35
  • Degree MSc
  • Study Level Masters
  • Study Mode Online
  • Duration 12 months

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This MSc 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. This course covers world-class training in core statistical, machine learning and computational methodology, and students will have the opportunity to apply their skills to real-life settings facilitated by the world-leading Institute of Psychiatry, Psychology & Neuroscience. The course is relevant to 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 for a graduate in/work in the fields of computer science, maths, physics, engineering and natural science, including psychology and medicine. This course is to meet the growing need for a graduate training course that focuses on methodological skills to respond to problems of “big data” of complexes diseases, which is underpinned by strong statistical methodology and real-world application.”  This course combines training in core statistical, machine learning and computational methodology, beginning at an introductory level, with a range of optional modules covering more specialised knowledge in statistical modelling and health informatics. Each year students will normally take modules totalling 60 credits for the PGCert. The course offers a unique delivery using a blended distance learning approach to allow flexibility of learning. Each programme module runs over 6-weeks and is made up of an off campus (online distance learning) familiarisation week, 5 days on campus, face-to-face teaching and 4 weeks off campus online distance learning.