MS4D02 - Data and AI Project 01 Sep 2021 - 31 Aug 2027 | Version 1
Associated Module Information
| Module Code: | MS4D02 | ||
|---|---|---|---|
| Module Title: | Data and AI Project | ||
| Faculty: | Faculty of Computing, Engineering and Science | ||
| Faculty Group: | Computing and Mathematics | ||
| Faculty Sub Group: | Maths | ||
| Module Leader: | Rebecca Peters | ||
| Module Team: | Samuel Jobbins, Angelica Pachon, Ieuan Griffiths | ||
| First Intended Intake: | Final Year of Intake: | ||
| Date Closed: | |||
| Credit Value: | 40 | Credit Level: | 7 |
| Language: | English | ||
| Percentage of Module Taught in Welsh: | 0 | ||
| Equivalent Module: | |||
| HECOS codes: | 100359 - artificial intelligence | 100992 - machine learning | 101034 - statistical modelling |
| HECOS Code Weighting: | 34 | 33 | 33 |
Document Version Information
| Version | 1 |
|---|---|
| Valid From | 01 Sep 2021 |
| Valid To | 31 Aug 2027 |
Module Aims
To enable graduates to utilise the work placement, and relevant learning from the taught part of the course, to design and develop a work-based project with direct benefit to their employer.
Content Summary
The Data/AI project itself will comprise a substantial practical work-based project scoped and developed during the first half of their placement and then implemented and evaluated during the latter half.
The project will be defined during the bootcamp phase of the program in consultation with the employer and academic supervisor. It provides a major opportunity for the graduates to apply learning throughout the course, to a practical issue of relevance and concern to the employer. Essentially, the project is a practically-focused academic dissertation.
Learning and Teaching Methods
| Activity Type | Hours |
|---|---|
| Lecture | 8 |
| Seminar | 4 |
| Project supervision | 16 |
| Work based learning | 360 |
| Independent Study | 12 |
| Total Hours Selected | 400 |
Learning Outcomes
| # | Learning Outcome |
|---|---|
| LO1 | Ability to research and apply appropriate theoretical and/or professional models or frameworks to define and develop a work-based project in Data/AI. |
Module Requisites
N/A
Assessment Criteria
| Assessment Category | Assessment Type | Description | Duration | Word Count | Weight (%) | Best of? | Pass Mark |
|---|---|---|---|---|---|---|---|
| Asynchronous Assessment | Research Plan / Proposal / Project/ Log 1 | Project initiation document | 0 | 2000 | 20 | No | 40 |
| Asynchronous Assessment | Report 1 | Formal report | 0 | 6000 | 60 | No | 40 |
| Synchronous Onsite Oral Assessment | Presentation (Synchronous Onsite) 1 | Formal presentation of work | 10 | N/A | 20 | No | 40 |
Assessment Matrix
| Assessment Type | Learning Outcomes | ||
|---|---|---|---|
| LO1 | |||
| Research Plan / Proposal / Project/ Log 1 | ✔ | ||
| Report 1 | ✔ | ||
| Presentation (Synchronous Onsite) 1 | ✔ | ||