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2023/24 Taught Postgraduate Programme Catalogue

MSc Urban Data Science and Analytics

Programme code:MSC-UDS&A-FTUCAS code:
Duration:12 Months Method of Attendance: Full Time
Programme manager:Dr Vikki Houlden Contact

Total credits: 180

Entry requirements:

A bachelor degree with a 2:1 (hons) in a subject containing a substantial numerate component. Successful applicants will have strong grades in relevant mathematical modules.

We accept a range of international equivalent qualifications. For more information please contact the Admissions Team.

IELTS 6.5 overall, with no less than 6.0 in any component. For other English qualifications, read English language equivalent qualifications.

School/Unit responsible for the parenting of students and programme:

Examination board through which the programme will be considered:

Programme specification:

The programme will:
Offer students the opportunity to dive deep into the methods and approaches of data science and learn how to apply them in understanding cities and setting urban policy. The programme will combine technical training the latest data science techniques – from data wrangling, to machine learning, to visualisation, and beyond – with the critical thinking needed to interrogate and understand complex urban and mobility challenges.

At the heart of the programme will be a commitment to tackling the real-world challenges facing cities. Fieldtrips in an urban context e.g. Amsterdam and Leeds will allow us to observe first-hand how data science can be used to create and shape urban policy, and how policies 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 a set of code and methods, 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. A new seminar series featuring urban researchers and practitioners will consolidate a wider network of urban data scientists and policymakers, and provide students with direct routes to the latest research, trends, and opportunities.

Year1 - View timetable

[Learning Outcomes, Transferable (Key) Skills, Assessment]

Compulsory modules:

Candidates will be required to study the following compulsory modules:

COMP5712MProgramming for Data Science15 creditsSemester 1 (Sep to Jan)
GEOG5401MAnalysing Cities15 creditsSemester 1 (Sep to Jan)
GEOG5402MData Science for Urban Systems15 creditsSemester 1 (Sep to Jan)
GEOG5403MCreative Coding for Urban Problems15 creditsSemester 2 (Jan to Jun)
GEOG5404MAnalytics for Urban Policy30 creditsSemester 2 (Jan to Jun)
GEOG5405MUrban Data Science Project60 creditsSemester 2 (Jan to Jun)

Optional modules:

Candidates will be required to study 30 credits from the following optional modules:

GEOG5042MGeographic Data Visualisation & Analysis
Co-requisite for: GEOG5225M
15 creditsSemester 1 (Sep to Jan)
GEOG5255MGeodemographics and Neighbourhood Analysis
Co-requisite for: GEOG5042M
15 creditsSemester 2 (Jan to Jun)
TRAN5032MTransport Data Collection and Analysis
Co-requisite for: TRAN5340M
15 creditsSemester 1 (Sep to Jan)
TRAN5340MTransport Data Science
Co-requisite for: TRAN5032M
15 creditsSemester 2 (Jan to Jun)

Last updated: 29/04/2022 15:45:24


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