The MSc Urban Data Science and Analytics programme offers students the opportunity to dive deep into the methods and approaches of data science and their application in understanding cities and setting urban policy. The programme will combine technical training in the latest data science techniques – from data wrangling to machine learning, visualisation, and beyond – with the critical thinking needed to interrogate and understand complex urban and mobility challenges.
At the heart of the programme is a commitment to tackling the real-world challenges facing cities. Fieldtrips in an urban context will allow students to observe first-hand how data science can be used to create and shape urban policy, and how policies can in turn impact urban systems and processes. We will work closely with external organisations and stakeholders, through co-development of creative solutions to urban problems.
Our technical training will not simply involve the rote learning of a set of code and methods; rather, students will be expected to apply these approaches creatively to ‘hack’ novel approaches with urban challenges. Students will compile a portfolio of urban data science projects, and in a final project, have the space to focus on developing advanced and creative solutions for urban policy, rather than on writing a long dissertation. There will be an opportunity to specialise as well, through a deeper dive into urban geographic or transport data and applications.
Students will uniquely benefit from exposure to the School of Geography, Institute for Transport Studies, and Leeds Institute for Data Analytics (LIDA), and the vast array of urban research ongoing in these institutions. opportunities.
[Learning Outcomes, Transferable (Key) Skills, Assessment]
View Timetable
Candidates will be required to study the following compulsory modules:
| Code | Title | Credits | Semester | Pass for Progression |
|---|---|---|---|---|
| COMP5712M | Programming for Data Science | 15 | Semester 1 (Sep to Jan) | PFP |
| GEOG5302M | Data Science for Practical Applications | 15 | Semester 1 (Sep to Jan) | PFP |
| GEOG5303M | Creative Coding for Real World Problems | 15 | Semester 2 (Jan to Jun) | |
| GEOG5401M | Analysing Cities | 15 | Semester 1 (Sep to Jan) | |
| GEOG5404M | Analytics for Urban Policy | 30 | Semester 2 (Jan to Jun) | |
| GEOG5405M | Urban Data Science Project | 60 | Semester 2 (Jan to Jun) |
Candidates will be required to study 30 credits from the following optional modules:
| Code | Title | Credits | Semester | Pass for Progression |
|---|---|---|---|---|
| GEOG5042M | Geographic Data Visualisation & Analysis | 15 | Semester 1 (Sep to Jan) | |
| GEOG5255M | Geodemographics and Neighbourhood Analysis | 15 | Semester 2 (Jan to Jun) | |
| TRAN5032M | Transport Data Collection and Analysis | 15 | Semester 1 (Sep to Jan) | |
| TRAN5340M | Transport Data Science | 15 | Semester 2 (Jan to Jun) |
Last updated: 30/04/2024 10:42:53
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