2024/25 Taught Postgraduate Module Catalogue

GEOG5301M Data to Insights in Multiple Environments

30 creditsClass Size: 50

Module manager: Arjan Gosal
Email: a.gosal@leeds.ac.uk

Taught: Semester 1 (Sep to Jan) View Timetable

Year running 2024/25

This module is not approved as an Elective

Module summary

This module integrates diverse research approaches across terrestrial and aquatic environments. It focuses on understanding the reasons and methods of data collection, followed by practical analysis to derive meaningful insights. By employing a blend of lectures, computer labs, and fieldwork to ensure comprehensive learning, combining theory with practical application, this interdisciplinary module includes a field course, emphasizing hands-on experience with sensors and data deployment, bridging theoretical knowledge with real-world environmental monitoring.

Objectives

This module aims to:

1. Provide an understanding of data collection methods and their significance in different environmental contexts.
2. Enable students to apply data science techniques for insightful analysis of environmental data.
3. Offer practical experience in both field data collection and secondary data analysis, addressing real-world environmental monitoring challenges.
4. Cultivate an awareness of good data practices, including considerations of bias and ethics in data science.

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

1. Critically analyse data collected from various environmental contexts and interpret it to derive meaningful insights.
2. Understand the methodologies and purposes behind environmental data collection, recognising the specific challenges and advantages of different environments.
3. Exhibit practical skills in deploying and utilising modular sensors in fieldwork, enhancing the students ability to collect and analyse environmental data.
4. Application of 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:

1. Technical skills, i.e., understanding and use sensors for collection of in-field data.
2. Work ready skills, specifically data analysis, analytics and data science skills, alongside effective communication of complex findings and insights into understandable and actionable information.
3. 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.

Syllabus

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

Teaching methods

Delivery type	Number	Length hours	Student hours
Supervision	4	0.50	2.00
Lectures	12	1.00	12.00
seminars	4	2.00	8.00
Practicals	8	3.00	24.00
Fieldwork	1	32.00	32.00
Fieldwork	2	7.00	14.00
Private study hours			208.00
Total Contact hours			92.00
Total hours (100hr per 10 credits)			300.00

Opportunities for Formative Feedback

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. Tutorials and 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, including group based presentations and tasks.

Methods of assessment

Coursework

Assessment type	Notes	% of formal assessment
Assignment	Coursework	50.00
Assignment	Coursework	50.00
Total percentage (Assessment Coursework)		100.00

The Report and Portfolio (assessments 1 and 2) will be in the same format – as the portfolio is skills based evidence of tasks, and the report is based on the students producing their own individual reports – both will remain unchanged from the original for the resit. In the event of students not being able to attend the fieldwork, data will be provided for students to be able to write the assessment, with them needing to elaborate the collection methodology for sensor deployment, as they would have been absent from the fieldwork.

Reading list

The reading list is available from the Library website

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

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