Module manager: Dr Paul Baxter
Email: p.d.baxter@leeds.ac.uk
Taught: 1 May to 30 Jun (2mth)(adv yr), 1 May to 30 June, 1 Nov to 31 Dec, 1 Nov to 31 Dec (2mth)(adv yr) View Timetable
Year running 2026/27
Students are required to meet the programme entry requirements prior to studying the module.
N/A
This module is not approved as an Elective
Data engineers work to ensure data is clean, consistent, and secure, and that the data infrastructure is reliable and efficient. This module introduces the principles of data engineering for designing and building systems that manage and process large amounts of data.
The objective of the module is to equip students with an understanding of how data engineering principles make data accessible to a wide range of users so they can make informed decisions and optimize their organization's performance. Concepts such as the data lifecycle, data platforms, data collection and storage, data processing and reporting, data analysis and visualisation, ethics, security and governance will be introduced and explored.
On successful completion of the module students will have demonstrated the following learning outcomes relevant to the subject:
1. Explain the role of the data engineer and how this relates to each element of the data lifecycle.
2. Describe the ethics and governance responsibilities of a data engineer and how this influences security policy and practice.
3. Recognise the use cases for different types of data platform, data collection and data storage solution.
On successful completion of the module students will have demonstrated the following skills learning outcomes:
1. Understand and explore how data processing and reporting tools are used to transform data so that users can make informed decisions (Work Ready, Digital)
2. Identify and justify how various AI and machine learning architectures and platforms are used in data analysis and visualisation to optimise organizational performance (Work Ready, Academic).
Indicative content for this module includes:
- Introduction to data engineering
- Data platforms and tools
- Data collection and storage
- Data processing and reporting
- Data analysis and visualisation
- Data security and governance
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Discussion forum | 6 | 1 | 6 |
| WEBINAR | 6 | 1 | 6 |
| Independent online learning hours | 42 | ||
| Private study hours | 96 | ||
| Total Contact hours | 12 | ||
| Total hours (100hr per 10 credits) | 150 | ||
For the 20% coursework assessment: There will be practice activities in a similar format in week 2 and 3. Formative feedback for these activities will be provided in the webinars and on the discussion forum prior to the assessment.
For the 80% coursework assessment: Progress towards assessment 2 will be scaffolded throughout the taught units of this module including self-assessment checkpoints. Students will have weekly formative activities for each taught unit of this module that directly relate to components of the coursework task. Feedback on these will be provided in the webinars and on the discussion forum.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Coursework | Students will be required to produce a written description (300 words) | 20 |
| Coursework | The assignment will require students to complete a written case-study (1,500 words) | 80 |
| Total percentage (Assessment Coursework) | 100 | |
This module will be reassessed by a 100% individual assessment covering all learning outcomes.
Check the module area in Minerva for your reading list
Last updated: 22/07/2026
Errors, omissions, failed links etc should be notified to the Catalogue Team