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2024/25 Taught Postgraduate Module Catalogue

GEOG5305M Environmental Data Science Project

60 creditsClass Size: 50

Module manager: Guy Ziv

Taught: Semester 2 (Jan to Jun) View Timetable

Year running 2024/25

This module is not approved as an Elective

Module summary

The project will take the place of the traditional dissertation, with the submission incorporating a data science output, in the form of a ‘notebook’-style analysis, and an associated written report. The submission will be expected to be similar to a dissertation in the expected level of work and quality. Students may choose to explore either work/topics covered as part of their programme or a new issue.


On completing this module, students will have demonstrated and applied the skills and knowledge gained elsewhere in the programme to effectively manage and complete a data science project based on contemporary and real world environmental issues, including critically evaluating literature, conducting analysis, evaluating results, and writing for a diverse range of audiences.

Learning outcomes
On successful completion of the module students will have demonstrated the following learning outcomes relevant to the subject:

1. Identify and critically review the literature relevant to the chosen topic.
2. Analyse environmental data using data science techniques, showing proficiency in both theoretical understanding and practical aspects.
3. Design and complete an independent data science project.
4. Development of post data analysis communications that are clear and relevant for multiple audiences.

Skills Learning Outcomes

On successful completion of the module students will have demonstrated the following skills learning outcomes:

1. Work ready skills, i.e. Project Management: demonstrate the ability to work independently, efficiently managing time and resources to lead a research project from inception to completion. Effective collaborative skills with internal (and potential external) stakeholders, using multiple forms of communication for diverse audiences.
2. Academic skills, i.e. utilise a wide range of scientific literature, and critical evaluation of information from varying sources, including results from independent analysis.
3. Technical and digital skills, i.e. demonstrate ability to process and analyse data using software and/or programming techniques.


Details of the syllabus will be provided on the Minerva organisation (or equivalent) for the module.

Teaching methods

Delivery typeNumberLength hoursStudent hours
Private study hours590.50
Total Contact hours9.50
Total hours (100hr per 10 credits)600.00

Opportunities for Formative Feedback

1. Dissertation supervisors will provide formative feedback during supervision meetings, and through contact via email if the student require additional help.

2. The poster submission has a dual purpose, serving as an interim check in, allowing supervisors to check student progress and any potential problems, as well as allowing students to get formative feedback from supervisors, and peers. The online format will allow fixed windows of time where students can view and comment on their peers' work. The online format of the poster presentation allows for a greater number of staff and students to view the posters.

3. Students will have taken training in the form of the research and data analysis process, both in the module's lectures and seminars, the latter affording opportunities for student to raise questions.

Methods of assessment

Assessment typeNotes% of formal assessment
Total percentage (Assessment Coursework)100.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Reading list

The reading list is available from the Library website

Last updated: 29/04/2024 16:14:37


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