Module manager: Lex Comber
Email: a.comber@leeds.ac.uk
Taught: Semester 2 (Jan to Jun) View Timetable
Year running 2024/25
This module is not approved as an Elective
This practical module explores how large volume and high-temporal frequency (big) data can provide insight into consumer behaviours and service delivery. The module combines theory and practical examples to support understanding of how to investigate, integrate, analyse and construct models from such data. The assessment tests skills in data manipulation, modelling, intepretation and report writing. In this way students gain an understanding of the ways through which a range of big data sources are used to reveal consumer behaviours and can be used to derive commercial or policy insight.
This module aims to:
1. Outline the role of data analytics in supporting decision making.
2. Introduce students to open source software and tools commonly used in academia and the commercial sector for handling and analysing transactional big data.
3. Provide students with practical experience in deriving spatial insight from a range of data sources using analytic tools and case studies informed by research and commercial practice.
On completion of the module, students will have demonstrated the following subject specific learning outcomes:
1. Be able to explain the role and value of data and data analytics in supporting decision making, using a variety of examples.
2. Demonstrate competency in handling a range of datasets , using open source software and analytic tools to summarise, analyse and visualise trends within the data.
3. Be able to clearly derive and visualise spatial insight from analysis of data.
Skills learning outcomes
On successful completion of the module students will have demonstrated the following skills learning outcomes:
1. Concise and evidence-based reporting
2. Use digital technology to communicate to a range of audiences
3. Critical thinking in the application of digital technologies
4. Use of open software and programming environments
5. Reasoning using quantitative evidence
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 | 3 | 15 |
| Lecture | 5 | 2 | 10 |
| Private study hours | 125 | ||
| Total Contact hours | 25 | ||
| Total hours (100hr per 10 credits) | 150 | ||
Lecture and practical activities have short integrated question and answer sessions, providing regular opportunities for formative assessment of student progress. Additionally, in-depth support and clarification is provided to students via the weekly 3hr computer practicals.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Assignment | Coursework | 100 |
| Total percentage (Assessment Coursework) | 100 | |
Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated
There is no reading list for this module
Last updated: 4/29/2024
Errors, omissions, failed links etc should be notified to the Catalogue Team