Urban Data Science and Analytics MSc

You will study 180 credits in total during your full-time Urban Data Science and Analytics MSc. If you are starting in September 2021, these will give you a flavour of the modules you are likely to study. All modules are subject to change.
 

Compulsory modules

Analysing Cities - 15 credits
You will explore urban systems and urban policy, primarily focusing on concepts of urban complexity (along themes such as mobility, housing, environment, health, etc.). The module will encourage critical reflection on urban systems and their complexity, and how their design and policies promote different outcomes. This module will be supplemented by fieldwork, where you will be exposed first-hand to urban complexity and prior policy decisions and observe applications of data science in real-world policymaking contexts.
 
Applied Data Science for Urban Policy – 30 credits 
This module will focus on urban systems and urban policy, expanding your knowledge on the concepts of urban complexity (with themes such as mobility, housing, environment, health, etc.) and looking across urban systems and considering decision-making in cities, including how data science can support policymaking. This module will be supplemented by local and international fieldwork, where you will be exposed first-hand to urban complexity and prior policy decisions and observe applications of data science in real-world policymaking contexts.
 
Data Science for Cities - 15 credits
You will look at the application of data science methods (including exploratory data analysis, machine learning, and visualisation) to urban problems, teaching you about the methods but focusing on their use in the urban setting, using urban data (with associated nuances).
 
This module will provide training on the production of good code and workflows for data science, and introductions to some methods.
 
Creative Coding – 15 credits
You will focus on the problem-based application of data science (e.g. ‘hacking’), training you to think creatively about the use of these methods for different urban challenges. It will push you to build on the knowledge you have gained from other modules to derive novel solutions to challenges. The primary coding language of the course will be Python, however the programme will explore routes towards enabling alternative submission of assessment materials in R. You will be exposed to other tools and languages via optional modules.
 
Area characteristics about neighbourhoods have widespread use in analyses which inform policy decision making in public (local and national government), and private (commercial) organisations as well as the third sector (charities). Area characteristics are also linked to individual level data to determine variations in health and educational application, for example. You will learn how to construct a variety of area measures and methods to incorporate these in analyses. The knowledge and skills involved are highly useful in many career settings including academic, public, private and third sector settings.
 
Programming for Data Science – 15 credits
Designed to give those with little or no programming experience a firm foundation in programming for data analysis and AI systems, recognising a diversity of backgrounds. The module will also fully stretch those of you with substantial prior programming experience (e.g. computer scientists) to extend your programming and system-building knowledge through self-learning supported by on-line courseware.
 
Urban Data Science Project – 60 credits
The dissertation module will bring together learning from each module, requiring you to produce a documented code workbook with a supporting 5000-word policy analysis (with appropriate references to previous work), highlighting how data science methods can be used to inform policy interventions and decision-making.
 

Optional modules

Optional modules (30 credits) are split over two set pathways and will 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. demographic, crime, health) and transport data analyses.
 
You will develop core visualisation and spatial analysis and statistical skills required for the analysis of geographically referenced data. You are introduced to ‘traditional’ and ‘novel’ datasets at different spatial scales and granularities related to areas, individuals, households and neighbourhoods.
 
Provides fundamentals of data collection and analysis in the context of transport. It addresses the loop covering research questions, data requirements, data collection/generation, data analysis, and interpretation.
 
Transport Data Science – 15 credits
This module will support sustainable transport policies using new techniques and datasets, ranging from openly available origin-destination datasets to huge datasets from global positioning systems (GPS). Not only does the module teach data skills, it also teaches the importance of understanding how advanced data analysis, modelling and visualisation can support the global transition away from fossil fuels.