Data Science Essentials: Real World Evidence

EIT Health promotes healthy living, active ageing and improvements in healthcare.We work to give EU citizens greater opportunities to enjoy a healthier and active life forlonger,and to postpone dependency on others, by leveraging big data and new technologies, identifying and removing barriers to innovation, and building on education and talent creation.

EIT Health

The European Institute of Innovation and Technology Health KIC (<A HREF="https://www.eithealth.eu/" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">EIT Health</A>), <A HREF="http://www.imperial.ac.uk/school-public-health/primary-care-and-public-health/research/ehealth/" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">Global eHealth Unit</A> at Imperial College London and <A HREF="http://healthiq.co.uk/" TARGET="_blank" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">HealthIQ</A> are pleased to offer a short course to provide you with the opportunity to understand the fundamentals of data science and to implement methods for analysing real world data (RWD). RWD includes the vast quantity of data that falls outside the boundaries of controlled clinical trials; data increasingly used to inform decision-making in healthcare. RWD can be utilised for numerous purposes such as measuring healthcare related resource-use and the burden of disease, evaluating prescribing patterns or clinical outcomes of a new medicine, describing current treatment patterns for a patient group for baseline information. However, dealing with RWD can be both daunting and challenging due to its volume, scale and the requirement to understand the source data and its collection in context. Further, to take into account the issues and limitations of RWD and to maximise it as a source of information, researchers, analysts and those with a commercial interest in such data need to be open to new and real world evidence (RWE) methodologies and frameworks which deviate from standard research methods. One of the primary objectives of the course will be the ability to apply an established RWE framework that ensures transparency and integrity around the collection, analysis and use of RWD. Other learning outcomes include the ability to (1) recognise key concepts of RWE in healthcare; (2) understand key considerations relating to Information Governance and RWE; (3) understand key differences between using RWE with and without data science; (4) identify and apply appropriate data analytic techniques to problems; (5) conduct exploratory analysis of RWD; (6) evaluate RWD, models or algorithms for accuracy in order to make informed decisions with regard to their use; (7) develop hypotheses and testing them through collaborative project work.
Data Science Essentials w/ Real World Data is a 5-week part-time course that will be delivered entirely online as a Massive Open Online Course (MOOC). You may take this course for free or pay £325.00 to take an optional certified track which will include completion of an assessed data science project. This certification shall provide you with: Additional resources and content reserved for certificate course bearing learners; On successful completion of an additional one-week module (which will be assessed), a certificate of completion from the European Institute of Innovation and Technology and Imperial College London; The ability to take this course and one additional certificate bearing course (for no extra charge) as part of the data science and public health curriculum from the Global eHealth Unit; Membership to the Global eHealth Unit Alumni Society.
<A HREF="http://www.imperial.ac.uk/continuing-professional-development/short-courses/medicine/public-health/data-science/" TARGET="_blank" TITLE="Learn more about Data Science Essentials" DATA-MSYS-CLICKTRACK="0" REL="nofollow noopener noreferrer">Learn more.</A>

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Data Science Essentials: Real World Evidence

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