Module manager: Dr Arjan Gosal (SoG)
Email: a.gosal@leeds.ac.uk
Taught: Semester 1 (Sep to Jan) View Timetable
Year running 2025/26
N/A
GEOG5301M (30 credits), split into: - DIME (Data to Insights in Multiple Environments) (15 credits) -Skills for Environmental Data Scientists (15 credits)
This module is not approved as an Elective
This module examines data collection and analysis across various environments, including deep sea, freshwater, and agricultural. Students explore and apply context-specific data science techniques through developing an understanding of each environment's unique data collection challenges. Through hands-on programming, statistical methods and machine learning approaches, students transform environmental datasets into actionable insights. The module emphasises selecting analytical approaches that address specific environmental questions rather than applying generic data science methods, ensuring techniques are tailored to the specific environmental context, with analysis objectives tailored to each distinct environment.
By employing a blend of lectures, seminars, and computer labs to ensure comprehensive learning, combining theory with practical application, this module ultimately aims to:
1. Provide an understanding of data collection methods and their challenges in different environmental contexts.
2. Enable students to apply appropriate data science techniques for data-driven analysis of environmental data.
3. Cultivate an awareness of good data practices, including considerations of bias in data science.
Subject specific learning outcomes:
On successful completion of the module students will have demonstrated the following learning outcomes relevant to the subject:
SSLO1: Critically analyse secondary data collected from various environmental contexts and interpret it to derive evidence-based insights.
SSLO2: Evaluate and differentiate between methodologies used for environmental data collection, recognising the specific challenges and advantages across different environments.
SSLO3: Apply appropriate data science techniques to analyse environmental data.
Skills learning outcomes:
On successful completion of the module students will have demonstrated the following skills learning outcomes:
SKLO1: Work ready skills, specifically data analysis, analytics and data science skills.
SKLO2: Effective communication of complex findings and insights into understandable and actionable information.
SKLO3: Sustainability skills in terms of utilisation and analysis of large scale data, that can be used towards informing the implementation of societally beneficial goals, e.g. SDGs.
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lectures | 12 | 1 | 12 |
| Practicals | 9 | 3 | 27 |
| Fieldwork | 1 | 7 | 7 |
| Independent online learning hours | 2 | ||
| Private study hours | 102 | ||
| Total Contact hours | 46 | ||
| Total hours (100hr per 10 credits) | 150 | ||
102
There are multiple opportunities in several learning environments for formative feedback in this module. In the context of computer practicals, staff will be able to offer immediate verbal feedback during sessions, focusing on the application of technical skills and problem-solving approaches. This immediate feedback, helps students adjust their techniques and understanding in real-time. Seminars will allow students to receive formative feedback that is immediate and allows staff to monitor the learning taking place within the cohort.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Coursework | Coursework | 40 |
| Coursework | Coursework | 60 |
| Total percentage (Assessment Coursework) | 100 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
The reading list is available from the Library website
Last updated: 30/04/2025
Errors, omissions, failed links etc should be notified to the Catalogue Team