Module manager: Dr Jennifer Sexton
Email: J.L.Sexton@leeds.ac.uk
Taught: 1 Jan to 28 Feb, 1 Jan to 28 Feb (adv year), 1 Jul to 31 Aug View Timetable
Year running 2026/27
Students are required to meet the programme entry requirements prior to studying the module. OLDA5101M or equivalent knowledge to the Trainee Data Scientist descriptor (https://ddat-capability-framework.service.gov.uk/role/data-scientist#trainee-data-scientist) in the Government Digital and Data Profession Capability Framework.
N/A
This module is not approved as an Elective
This module develops the skills necessary to be a successful data scientist that can deliver projects to a professional standard, as would be expected by an employer or client.
The objective of the module is to equip students with the skills necessary to undertake project work as a data scientist. Project planning, reviewing existing methodologies and the presentation of outputs in different forms all form part of this. This module will also include ethical considerations of data usage.
On successful completion of the module students will have demonstrated the following learning outcomes relevant to the subject:
1. Understand how to present data science projects effectively in both oral and written formats, including communicating effectively with a non-technical audience.
2. Explain how to critically appraise different methodological choices when planning project work in data science.
On successful completion of the module students will have demonstrated the following skills learning outcomes:
1. Communicate the key concepts, skills and attitudes for developing a proposal to deliver data science project work (Work ready, Digital)
2. Engage with professional ethics for working in data science. (Work ready, sustainability)
Indicative content for this module includes:
- Developing a project proposal.
- Searching the literature.
- Critical appraisal skills.
- Professional ethics in data science.
- Continuing professional development.
- Presenting project work effectively in oral and written formats.
| Delivery type | Number | Length hours | Student hours |
|---|---|---|---|
| Discussion forum | 6 | 2 | 12 |
| WEBINAR | 1 | 1.5 | 1.5 |
| WEBINAR | 5 | 1 | 5 |
| Independent online learning hours | 42 | ||
| Private study hours | 89.5 | ||
| Total Contact hours | 18.5 | ||
| Total hours (100hr per 10 credits) | 150 | ||
Online learning materials will provide regular opportunity for students to check their understanding (for example through formative MCQs with automated feedback). A discussion board is set to support students with doing tasks and assessment in the module, this discussion board will be monitored by the module leader/tutor. Students will be asked to submit a outline of reflective assignment in week 5 for peer-to-peer review and feedback.
| Assessment type | Notes | % of formal assessment |
|---|---|---|
| Reflective log | Students will be required to produce a short reflective log (300 words) reflecting on what they have learned about project planning and how they plan to put this in to practice when working as a data scientist. | 20 |
| Assignment | The assignment will require students to complete a reflective assignment (1,500 words) which critically reflects on the skills they have learned during the module and how they will use them in practice when working as a data scientist. It is expected that the assignment will be completed in one week. | 80 |
| 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: 22/07/2026
Errors, omissions, failed links etc should be notified to the Catalogue Team