Urban Data Science and Analytics MSc

Program Overview

This Master's program combines data science methods with urban policy to provide in-depth knowledge of urban data science. It integrates technical skills in data wrangling and visualization with analytical approaches for urban systems, enabling graduates to tackle real-world urban challenges using innovative data-driven solutions and critical thinking skills

Program Outline

Degree Overview:

Urban data science and analytics are crucial for helping cities evolve, providing valuable insights into urban processes, dynamics within cities, and highlighting local and global issues. This MSc program offers the opportunity to gain in-depth knowledge of data science methods and approaches and learn how to apply them in understanding cities and setting urban policy. The course combines technical training in the latest data science techniques with critical thinking skills needed to interrogate and understand complex urban and mobility challenges.

Objectives:

• Gain in-depth knowledge of data science methods and approaches.
• Develop technical skills in data science techniques, including data wrangling, machine learning, visualization, and more.
• Develop critical thinking skills to interrogate and understand complex urban and mobility challenges.
• Gain experience tackling real-world challenges facing cities.
• Develop transferable skills as a researcher.
• Build industry connections through working with external organizations and stakeholders.
• Observe first-hand how data science can be used to create and shape urban policy.

Outline:

Semester 1:

• Data Science for Urban Systems: Provides a foundation in data science training, combining real-world data sets, analytics, and applications across a diverse range of contemporary urban contexts.
• Analysing Cities: Focuses on understanding and analyzing cities from a complex system perspective using an interdisciplinary approach, considering how we plan, organize, and evaluate cities, and address future challenges.
• Programming for Data Science: Introduces programming (currently using Python).

Semester 2:

• Analytics for Urban Policy: Explores the key considerations in using data science across a range of urban policy areas and addresses how these policies result in tangible change.
Includes field trips to observe the impact of different urban policies within diverse environments and contexts.
• Creative Coding: Takes a ‘hackathon’ approach to deliver creative solutions to real-world challenges.
Students work in groups with industry experts to gain first-hand experience applying critical and innovative thinking to evaluate and explore a range of different urban domains.
• Optional Modules: Offer deeper training in spatial analysis or transport training, enabling an expansion of disciplinary expertise.
Examples include Geographic Data Visualisation & Analysis, Geodemographics and Neighbourhood Analysis, Transport Data Collection and Analysis, and Transport Data Science.

Project Work:

• Students work on a 60-credit dissertation-style research project over the summer months.
• The project brings together learning from each module, requiring students to produce a documented code workbook with a supporting 5000-word practical briefing.
• The project highlights how data science methods can be used to inform policy interventions and decision-making.
• Students have the opportunity to work with industry partners on this project.
• Example dissertation themes include: urban economics, cities and crime, urban inequalities, transport and mobility, housing and real estate, health and wellbeing, smart cities, pollution, vulnerable populations, and geodemographics.

Fieldwork:

• Field courses provide students with an opportunity to explore urban analytics ‘on the ground’ in inspiring urban environments.
• Students consider cities from different perspectives and observe urban systems evolve across traditional policy areas.
• Students get involved in collecting data, making observations and assessments, and exploring cities in ways they have never done before.
• Travel and accommodation costs associated with compulsory field trips are covered by the university.

Course Structure:

• The course consists of compulsory and optional modules.
• Full details of all modules for each year will be provided before students enter each year.

Assessment:

• The primary mode of assessment is through the production of code and critical analysis for the exploration of urban phenomena and problems.
• Some modules focus more on qualitative analysis of urban systems, while others incorporate aspects of team-based assessment.
• Code and analyses are documented through a personal (protected) GitHub repository, which acts as a portfolio of work for future employers on completion of the course.

Teaching:

• The course incorporates a range of innovative modes of delivery, with a general focus on maximizing time for practical, problem-based learning.
• Face-to-face learning in workshops, small groups, drop-ins, laboratories, lectures, and seminars is combined with teaching and learning delivered using interactive digital platforms.
• Students are 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.
• Students 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.

Careers:

• Data science and analytics are crucial to many industries worldwide, meaning demand for qualified specialists in this field has grown exponentially in recent years.
• The course teaches in-depth technical knowledge and skills in data science along with training in workflow practice, teamwork, and ‘hacking’ that’s highly sought after by employers.
• Students build an online portfolio of work developed throughout the course, which demonstrates their skills to prospective employers.
• Upon completion of the course, students have the technical knowledge to secure employment in local government, companies handling spatial data (e.g., supermarkets, retail), start-ups, transportation authorities and operators, urban planners and consultancies (e.g., Arup, Mott MacDonald) in roles such as a data scientist, data analyst, or software developer.
• University of Leeds students are among the top 5 most targeted by top employers according to The Graduate Market 2024, High Fliers Research, meaning our graduates are highly sought after by some of the most reputable companies in the field.

Other:

• The course is taught by the School of Geography, School of Computing, and Institute for Transport Studies.
• 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.
• Students may also be taught by industry professionals with years of experience, as well as trained postgraduate researchers, connecting them to some of the brightest minds on campus.
• The University's Careers Service is one of the largest in the country, providing a wide range of resources to ensure students are prepared to take their next steps after graduation.
• The Careers Service offers dedicated Employability Officer support, employability and networking events, employability skills training, and the MyCareer system.
• Students have access to exciting opportunities offered by the Leeds University Union, including volunteering and over 300 clubs and societies to get involved in.

£13,500 (Total) International fees:

£30,750 (Total)