Module manager: Dr Vikki Houlden
Email: V.Houlden@leeds.ac.uk
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2024/25
GEOG5403M
This module is not approved as an Elective
This module will provide students with an opportunity to enhance their creativity and develop their technical skills in solving data science problems. Hackathons will focus on several domains (e.g. transport, environment, health, sustainability), providing students with an opportunity to tackle challenges in an original and novel way. Problems and datasets will be sought from industry and academic partners, providing a new route to collaboration and an opportunity for students to develop work-ready competencies, which will be valuable to future employers.
The aim of this module is to:
1. Apply and extend programming and contextual knowledge to deliver creative solutions to tackle real-world challenges using contemporary data sets
2. Build in students a strong foundation of collaboration and teamwork, fostering cohort identity and competency working with both peers and industry partners
3. Apply critical and innovative thinking to evaluate and explore a range of different domains, presenting outcomes to inform both academic and industrial audiences
On successful completion of the module students will have demonstrated the following learning outcomes relevant to the subject:
1. Collaborate in teams during hackathons, including dividing tasks to achieve self-determined goals
2. Apply theories and skills obtained during Term 1 in selecting the most appropriate methods and practices for developing ambitious and realistic solutions to pressing real world challenges
3. Apply conceptual knowledge to a range of different domains and datasets, developing and expanding practical application skills
4. Develop communications with peer, academic and industrial partners, through written, oral, and visual methods
5. Examine a diverse range of applications and industries which generate, work with, and deliver solutions to real world urban and environmental problems
Skills Learning Outcomes
On successful completion of the module students will have demonstrated the following skills learning outcomes:
1. Applying and developing appropriate data science and programming techniques
2. Summarising and appraising analytical approaches for academic and industry partners
3. Communicating scientific knowledge to lay audiences
4. Collaborating with teammates and industry partners scope out the context and analytical needs of real-world problems
5. Develop creativity skills to generate ideas, demonstrate originality, and imaginative thinking
6. Deliver effective and engaging oral and visual presentations to a variety of audiences
Details of the syllabus will be provided on the Minerva organisation (or equivalent) for the module.
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Practicals | 5 | 7 | 35 |
| Practical | 5 | 2 | 10 |
| Private study hours | 105 | ||
| Total Contact hours | 45 | ||
| Total hours (100hr per 10 credits) | 150 | ||
Formative feedback will be provided during each practical session, in response to student group work and during questions for the presentations.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Report | Coursework | 50 |
| Presentation | Coursework | 10 |
| Presentation | Coursework | 10 |
| Presentation | Coursework | 10 |
| Presentation | Coursework | 10 |
| Presentation | Coursework | 10 |
| Total percentage (Assessment Coursework) | 100 | |
Resit: a topic-specific blog post, and an individual video-recorded presentation. The original blog post will review the processes and lessons learned from all hackathons, taking examples from specific sessions to demonstrate key points. The resit will focus on reviewing one topic and its relation to the wider urban context.
The reading list is available from the Library website
Last updated: 11/28/2024
Errors, omissions, failed links etc should be notified to the Catalogue Team