2024/25 Taught Postgraduate Programme Catalogue
MSc Environmental Data Science and Analytics
| Programme code: | MSC-EDS&A-FT | UCAS code: | |
|---|---|---|---|
| Duration: | 12 Months | Method of Attendance: | Full Time |
| Programme manager: | Dr Arjan Gosal | Contact address: | A.Gosal@leeds.ac.uk |
Total credits: 180
Entry requirements:
Entry Requirements are available on the Course Search entry
School/Unit responsible for the parenting of students and programme:
School of Geography
Examination board through which the programme will be considered:
Programme specification:
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 in analysing and interpreting 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 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 research dissertation will enhance students’ skills in the integration of data science techniques with environmental data.
Year1 - View timetable
[Learning Outcomes, Transferable (Key) Skills, Assessment]
Compulsory modules:
Candidates will be required to study the following compulsory modules
| COMP5712M | Programming for Data Science | 15 credits | Semester 1 (Sep to Jan) | |
| GEOG5301M | Data to Insights in Multiple Environments | 30 credits | Semester 1 (Sep to Jan) | |
| GEOG5302M | Data Science for Practical Applications | 15 credits | Semester 1 (Sep to Jan) | |
| GEOG5303M | Creative Coding for Real World Problems | 15 credits | Semester 2 (Jan to Jun) | |
| GEOG5304M | Machine Learning for Environmental Data | 15 credits | Semester 2 (Jan to Jun) | |
| GEOG5305M | Environmental Data Science Project | 60 credits | Semester 2 (Jan to Jun) |
Optional modules:
Candidates will be required to study 30 credits from the following optional modules:
| GEOG5060M | GIS and Environment | 15 credits | Semester 2 (Jan to Jun) | |
| GEOG5710M | Digital Image Processing for Environmental Remote Sensing | 15 credits | Semester 2 (Jan to Jun) | |
| GEOG5830M | Environmental Assessment | 15 credits | Semester 2 (Jan to Jun) | |
| GEOG5870M | Web-based GIS | 15 credits | Semester 2 (Jan to Jun) |
Last updated: 29/04/2024 16:06:11
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