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Data Science for Public Health Outcomes

This pedagogic intervention seeks to combine the disciplines of Public Health Research based at WMS with Data Science Research based at WBS through an Interdisciplinary and progressive workshop series. This series will be composed of weekly 3-hour workshops and will cover a four week period. The main aim is to promote transferable research skills of the students by giving students hands on experience of analysing large related health datasets by exploiting appropriate statistical methods. More specifically, participants will be working on a wide range of freely available health datasets using R programming. R is a free programming language equipped with very powerful statistical features.

The workshop series will be delivered by a collaborative group of four PhD candidates from the Data Science Lab of Warwick Business School and the Division of Health Sciences at Warwick Medical School. All of the organisers have experience in using R and quantitative approaches during their research. This alliance was formed during the interdisciplinary workshop delivered by Dr. Nicholas Monk and Dr. Elena Riva.

Upon completion of the workshop, students are expected to be able to work on other health related big data analyses tasks confidently, procure data using online sources such as Google and Twitter, understand how to choose the appropriate statistical methods and avoid common statistical pitfalls. In the long term we foresee this workshop enabling collaborations between researchers based at WBS and the Medical School/Life Sciences. This could lead to strategic partnerships between departments and innovative research projects.

Merve Alanyani is a PhD candidate at Warwick Business School. She has a background in Computer Science and Complex Systems. Her work focuses on analysing large open data sources such as Flickr with concepts from image processing and machine learning to understand and predict human behaviour at a global scale.

Jennifer Cooper is a PhD student in the Division of Health Sciences at Warwick Medical School. She has a background in Biological Sciences and Clinical Trials. Her PhD is focusing upon developing risk prediction models for use in cancer screening programmes. She is conducting analysis of data in R and uses machine learning techniques

Chanuki Seresinhe is a PhD student in the Data Science Lab in Warwick Business School’s Behavioural Science Group. She has a background in Behavioural Economics and Digital Design. Her current research focuses on using online data from such sources as Flickr and Twitter to understand how the aesthetics of the environment impacts human wellbeing

Josephine Khan is a PhD student in the Division of Health Sciences at Warwick Medical School. She has a background in Mathematics and applied Medical Statistics. Her PhD research at WMS focuses on developing methods of analysis for survival data in clinical trials that use Adaptive Seamless Designs.