The Environmental Data Science and Analytics MSc programme is designed for students eager to apply data science in tackling environmental challenges. As the global focus shifts increasingly toward sustainability and the environment, the clear need for skilled individuals capable of effectively analysing and interpreting complex environmental data becomes ever more crucial. This MSc blends technical training in data science, whilst retaining a focus on the nuances that multiple environmental contexts bring.
It covers important aspects of environmental modelling, data curation, machine learning, data visualisation, and forming insights; underpinned by a strong foundation in programming-based analysis. Students will be immersed in a practical learning environment, engaging with diverse real-world environmental datasets, and gaining hands-on experience that is directly applicable to contemporary environmental issues.
Students will build the skills needed to analyse, understand, interpret, and visualise complex environmental data; generating insights that address real-world environmental challenges. A mix of individual and collaborative working will encourage the formation of vital communication skills. The in-depth Data Science Project will empower students to independently integrate sophisticated data science techniques with environmental data,. developing graduates who can drive data-informed environmental decision-making.
[Learning Outcomes, Transferable (Key) Skills, Assessment]
View Timetable
Candidates will be required to study the following compulsory modules
| Code | Title | Credits | Semester | Pass for Progression |
|---|---|---|---|---|
| GEOG5302M | Data Science for Practical Applications | 15 | Semester 1 (Sep to Jan) | |
| GEOG5303M | Creative Coding for Real World Problems | 15 | Semester 2 (Jan to Jun) | |
| GEOG5304M | Machine Learning for Environmental Data | 15 | Semester 2 (Jan to Jun) | |
| GEOG5305M | Environmental Data Science Project | 60 | Semester 2 (Jan to Jun) | |
| GEOG5311M | DIME (Data to Insights in Multiple Environments) | 15 | Semester 1 (Sep to Jan) | PFP |
| GEOG5312M | Skills for Environmental Data Scientists | 15 | Semester 1 (Sep to Jan) | PFP |
| GEOG5415M | Programming for Spatial Data Science | 15 | Semester 1 (Sep to Jan) | PFP |
Candidates will be required to study 30 credits from the following optional modules:
| Code | Title | Credits | Semester | Pass for Progression |
|---|---|---|---|---|
| GEOG5060M | GIS and Environment | 15 | Semester 2 (Jan to Jun) | |
| GEOG5710M | Digital Image Processing for Environmental Remote Sensing | 15 | Semester 2 (Jan to Jun) | |
| GEOG5830M | Environmental Assessment | 15 | Semester 2 (Jan to Jun) | |
| GEOG5870M | Web-based GIS | 15 | Semester 2 (Jan to Jun) |
Candidates will be required to study 0 credits of discovery modules
Last updated: 28/04/2025 12:02:05
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