Urban Data Science

Overview

This completed module took students from raw, inconsistent municipal datasets to defensible, well-communicated findings. Much of the work was unglamorous — reconciling formats, documenting assumptions, quantifying uncertainty — which is precisely the point.

Learning outcomes

• Acquire, clean, and join heterogeneous urban datasets reproducibly.
• Analyse spatial and mobility data with appropriate methods.
• Represent uncertainty and provenance in every result.
• Navigate the privacy and consent issues of human-centred data.

Assessment

Component	Weight
Data studio exercises	40%
Capstone project	60%

Note

This module is not currently scheduled. Materials are retained here for reference.