MS4T01 - MSc Project – Data Science 01 Apr 2025 - 31 Aug 2027 | Version 3
Associated Module Information
| Module Code: | MS4T01 | ||
|---|---|---|---|
| Module Title: | MSc Project – Data Science | ||
| Faculty: | Faculty of Computing, Engineering and Science | ||
| Faculty Group: | Computing and Mathematical Sciences | ||
| Faculty Sub Group: | Mathematical Sciences | ||
| Module Leader: | Daniel Cunliffe | ||
| Module Team: | Samuel Jobbins, Ieuan Griffiths, Rebecca Peters, Angelica Pachon, Sharan Johnstone | ||
| First Intended Intake: | SEP 2018 | Final Year of Intake: | 2024 |
| Date Closed: | |||
| Credit Value: | 60 | Credit Level: | 7 |
| Language: | English | ||
| Percentage of Module Taught in Welsh: | 0 | ||
| Equivalent Module: | |||
| HECOS codes: | 100403 - mathematics | ||
| HECOS Code Weighting: | 100 | ||
Document Version Information
| Version | 3 |
|---|---|
| Valid From | 01 Apr 2025 |
| Valid To | 31 Aug 2027 |
Module Aims
To provide the opportunity for students to employ autonomy and originality in the application of their data science and research skills to a complex problem. To provide an opportunity for the student to investigate a relevant, real world application of their skills.
To produce a justified solution to a significant problem that is informed by a critical review of research. To further develop the student's investigative, research, writing and presentation skills as a self-directed, autonomous learner.
Content Summary
The topic of the project can be from any area of data science. Where possible, projects will be proposed by industry collaborators. All projects will include a literature review and an appropriate evaluation of the ‘implementation’.
Before, or at the start of, the academic year the project student will have chosen a suitable project topic for study. A list of project topics is supplied by staff, but the students are encouraged to suggest their own topics for approval by the Project Co-ordinator. This will normally involve a suitable problem or problems for solution in a data science application area, involving an independent “in-depth” investigation by the student.
The project will be carried out under the guidance of an appropriate project supervisor, who will be a project assessor. The project student will submit to the supervisor a draft project plan for discussion by the end of October. An interim written project progress report will be submitted by the project student, usually by the end of the first week after Christmas.
Each student will be required to make a presentation of the project findings before a small audience of staff and students. The final written project report will normally be submitted towards the end of the timetabled teaching weeks.
This module will facilitate the development of Personal Development Planning (PDP) through the delivery of the key skills identified below in this module descriptor.
Learning and Teaching Methods
| Activity Type | Hours |
|---|---|
| Lecture | 2 |
| Project supervision | 6 |
| Independent Study | 588 |
| Directed Study | 4 |
| Total Hours Selected | 600 |
Learning Outcomes
| # | Learning Outcome |
|---|---|
| LO1 | Learning Outcome 1:To analyse and interpret an appropriate problem, or problems, in a particular real world topic selecting relevant Data Science skills. |
| LO2 | Learning Outcome 2:To make a professional presentation of the project aims, objectives and conclusions before a small audience of staff and students and produce a final written report on the project achievements by the agreed deadline. |
Module Requisites
N/A
Assessment Criteria
| Assessment Category | Assessment Type | Description | Duration | Word Count | Weight (%) | Best of? | Pass Mark |
|---|---|---|---|---|---|---|---|
| Synchronous Onsite Oral Assessment | Oral Assessment (Internally assessed, Onsite) 1 | 15 minute presentation, 10 minutes question and answer session | 25 | N/A | 20 | No | 40 |
| Asynchronous Assessment | Student Choice 1 | Major Project or Technical Report 10 page limit (5000 words) | 0 | 12000 | 80 | No | 40 |
Assessment Matrix
| Assessment Type | Learning Outcomes | ||
|---|---|---|---|
| LO1 | LO2 | ||
| Oral Assessment (Internally assessed, Onsite) 1 | ✔ | ✔ | |
| Student Choice 1 | ✔ | ✔ | |