Data Science Essentials: Real World Evidence
06 August 2017 – 09 September 2017 and 2 October - 5 November 2017
- Duration: 5-week part time online (Approximately 2-4 hours per week of online learning; the structure is modular to allow for catch-up and progressive course building for flexibility).
- Location: Online
- MOOC Free
- Certificate Track £325
Why Data Science & Real World Evidence?
This course will introduce participants to the intersection of real world evidence (RWE) and healthcare and provide the opportunity to understand and develop new methods for data analysis. Real World Data (RWD) defines the huge quantity of data that falls outside the boundaries of controlled clinical trials, data that is increasingly used to inform decision-making in healthcare.
Option 1: 6 August 2017 - 9 September 2017
Option 2: 2 October 2017 - 5 November 2017
- Recognise key concepts of RWE in healthcare.
- Understand key considerations relating to Information Governance and RWE.
- Understand key differences between using RWE with and without data science.
- Identify and apply appropriate data analytic techniques to a problem using an RWE
- Conduct exploratory analysis of RWD.
- Evaluate RWD, models or algorithms for accuracy in order to make informed.
- decisions with regard to their use.
- Develop hypotheses and testing them through collaborative project work
The programme is being delivered as a Massive Open Online Course (MOOC); this allows you to experience the course materials and lectures on-line and with a global learning community. The format is designed to be flexible to allow you to pursue the course content alongside your work. We have designed the course this way to allow you the time to reflect and absorb teaching, as opposed to intense multi-day continuing professional development which can be both intense and time consuming.
* Please note the below modules are accurate at time of publication and will be expanded throughout the summer.
Principles of Real World Evidence, Health and how they collide
This module will introduce RWE, current RWE trends and themes explain both the benefits and limitations of using RWE. The module will then cover how RWE and healthcare systems combine to provide the potential for rich insights in clinical and personal data management.
Information Governance and Data results deployment
This module will explore the policy implications of RWE projects and provide a view of the current Information Governance issues being faced in the enablement of RWE projects. This module will also introduce key datasets that RWE can exploit across primary and secondary care (HES/CPRD) and the ethical considerations that need to be taken into account with regard to their use.
Design Thinking, Methodology and Framework
This module will introduce RWE methodologies and provide a framework for ‘design thinking’. This module will also explore how such tools can be used to identify and apply appropriate data analytic techniques to problems such as commercial challenges in the real world.
Fundamentals of Exploratory Analysis and RWE Evaluation
This module will introduce key concepts in exploratory analysis and the conceptualisation of an RWE solution to a practical problem, as well as review the pitfalls of RWD exploratory analysis in order to effectively critique the results. Learners will be provided access dummy data sets in order to collaboratively conduct analysis and in a final project they will be asked to create and test a hypothesis based on the results obtained from exploration. All learners will be provided access to a Business Intelligence tool with a user-friendly front-end for interrogating and aggregating RWD.
Who should attend?
This course is open to all participants with an interest in the application of Information and Communication Technologies (ICT) within healthcare such as those with a hands-on interest in data analysis and those whose interest in the RWD is commercially focused. This can be an undergraduate student in data science, an analyst or commercial manager working in life sciences pharmaceuticals, healthcare regulation, biotech and medical devices).
Imperial’s Global eHealth Unit and EIT Digital Professional School identified a niche for providing professionals interested in technology projects with an approachable framework to begin to engage with these subjects. These two courses are designed to provide a foundation to get past the buzzword hype, provide principles to inform what is happening with the state of the art, and then give practical, case-study driven examples to provide a means to understand key concepts. Our courses are delivered in two parts: the first being a focus on principles, the second being a focus on industry application. To deliver this, we have researchers introducing key concepts, theories and frameworks, whilst we have industry collaborators making these concepts come to life with real world examples and cases. This combination will have you leaving our courses with ideas and perspectives that you can immediately implement at your organisation or on your next project.
If you are looking to launch a new company in eHealth and data science in healthcare, are a technology manager with oversight on teams delivering these initiatives or have general interest in moving into this space, then these courses are for you.