The course combines technical training in data science with a rich exploration of urban systems and policy, enabling you to create novel data analyses that are informed by a contextual understanding of cities.
In the first term, the course will introduce you to the theory and application of urban data science, through two complementary modules: Data Science for Urban Systems, and Analysing Cities. The first of these will provide a foundation in data science training, combining real-world data sets, analytics, and applications across a diverse range of contemporary urban contexts.
Analysing Cities will focus on understanding and analysing cities from a complex system perspective using an interdisciplinary approach, to consider how we plan, organise, and evaluate cities, and address future challenges. You will also be given an introduction into programming (currently using Python) on the Programming for Data Science module, parented by the School of Computing.
The second term will build and expand upon this knowledge. The Analytics for Urban Policy module will explore the key considerations in using data science across a range of urban policy areas and address how these policies result in tangible change. Field trips will enable you to observe the impact of different urban policies within diverse environments and contexts.
The Creative Coding module takes an exciting and unique ‘hackathon’ approach to deliver creative solutions to real-world challenges. Working in groups and with Industry experts, you will gain first-hand experience of applying critical and innovative thinking to evaluate and explore a range of different urban domains.
Optional modules incorporate deeper training in spatial analysis or transport training, enabling an expansion of disciplinary expertise. These modules will make you more familiar with the types of datasets and problems involved in geographic (e.g. demographics, crime, health) and transport data analyses.
Over the summer months you will work on a 60-credit dissertation-style research project, which brings together learning from each module, requiring you to produce a documented code workbook with a supporting 5000-word practical briefing, highlighting how data science methods can be used to inform policy interventions and decision-making.
The dissertation project is one of the most satisfying elements of this course. It allows you to apply what you’ve learned to a piece of research focusing on a real-world problem, and it can be used to explore and develop your specific interests. You will also have the opportunity to work with industry partners on this project.
Example dissertation themes for Urban Data Science and Analytics MSc include:
Cities and crime
Transport and mobility
Housing and real estate
Health and wellbeing
Fieldwork provides a great opportunity to study a fascinating subject in contrasting environments away from the University. On this programme we currently offer a UK-based fieldwork opportunity.
Want to find out more about your modules?
Take a look at the Urban Data Science and Analytics module descriptions for more detail on what you will study.
The list shown below represents typical modules/components studied and may change from time to time. Read more in our Terms and conditions.
For more information and a full list of typical modules available on this course, please read Urban Data Science and Analytics MSc in the course catalogue
Programming for Data Science
Data Science for Urban Systems
Creative Coding for Urban Problems
Analytics for Urban Policy
Urban Data Science Project
Optional modules (selection of typical options shown below)
Geographic Data Visualisation & Analysis
Geodemographics and Neighbourhood Analysis
Transport Data Collection and Analysis
Transport Data Science
Learning and teaching
The course will incorporate a range of innovative modes of delivery, with a general focus on maximising time for practical, problem-based learning. For example, the ‘Creative Coding’ module will incorporate minimal lecture-style teaching and instead focus on problem-based learning, where you will work with other students in teams to tackle problems and datasets provided by external stakeholders. Within this module, you will be coached by teaching staff to identify novel and compelling ways to tackle the challenges and be required to present your work.
Face-to-face learning in workshops, small groups, drop-ins, laboratories, lectures, and seminars will be combined with teaching and learning delivered using interactive digital platforms that build up your skills using relevant technologies and enable effective delivery of key materials.
You will have access to laboratories in the Leeds Institute for Data Analytics, as well as excellent teaching facilities within the School of Geography, including a GIS computer cluster with industry-standard software.
Our Virtual Learning Environment will help to support your studies: it’s a central place where you can find all the information and resources for the School, your programme, and modules.
You can also benefit from support to develop your academic skills, within the curriculum and through online resources, workshops, one-to-one appointments and drop-in sessions.
Active research environment
You will be taught by an experienced team of academics and researchers who are actively engaged in cutting-edge research and part of the Centre for Spatial Analysis and Policy research group, Leeds Institute for Data Analytics, and the Alan Turing Institute.
The Programme Leader, Dr Vikki Houlden, is a Lecturer in Urban Data Science whose research focuses on understanding the ways in which spaces and places embody inequalities, and the social structures influencing how people relate to their environment, with particular interest in how urban landscapes impact health and wellbeing.
On this course you’ll be taught by our expert academics, from lecturers through to professors. You may also be taught by industry professionals with years of experience, as well as trained postgraduate researchers, connecting you to some of the brightest minds on campus.
The primary mode of assessment will be through the production of code and critical analysis for the exploration of urban phenomena and problems. Some modules will focus more on qualitative analysis of urban systems, while others will incorporate aspects of team-based assessment.
Code and analyses through a personal (protected) GitHub repository will be documented, which will act as a portfolio of work for future employers on completion of the course.