Further projects have been listed at the Universities of Leeds and Manchester:

New methods of space-time data analysis to operationalise passively collected transport data on the route network with See.Sense

Data assimilation for agent-based models with Improbable

Data anonymisation and data privacy for clinical trial data with Roche

Towards data-driven policy development: the case of London’s built cycling infrastructure with CycleStreets.net

 

The projects are all partnered with an external organisation giving you the opportunity to work closely with industry, opportunities range from exploring different approaches to health and obesity, to developing automated processes for use with forensic testing.

We are looking for graduates from a wide range of backgrounds with interests in how data can be used to address social science questions using statistical or predictive techniques. You will work with a multi-disciplinary team of supervisors as well as having the opportunity to work with external partners on real life problems.

The programme is an integrated one (+4), where you will undertake an MSc in Data Analytics over the first two years which will provide you with the foundation skills to complete
your research project.

Details for all available projects can be found on our available projects page

Please contact us with any questions

Leeds projects:

Can Food Purchase Data Inform us about Food Intake, Food Security and Food Sustainability?

Whole systems approach to obesity

Data assimilation for agent-based models

New methods of space-time data analysis to operationalise passively collected transport data on the route network

Towards data-driven policy development: the case of London’s built cycling infrastructure

Manchester projects:

Understanding the construction of metropolitan areas through data analytics: the case of GMCA

Integrating data and simulation towards a mobility services decision support system

An automated data analytics platform for forensic testing

Data anonymisation and data privacy for clinical trial data

 

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