Data Science and Modelling Dissertation
| Module title | Data Science and Modelling Dissertation |
|---|---|
| Module code | EMGM003 |
| Academic year | 2025/6 |
| Credits | 60 |
| Module staff | Dr Markus Mueller (Lecturer) |
| Duration: Term | 1 | 2 | 3 |
|---|---|---|---|
| Duration: Weeks | 5 | 12 |
| Number students taking module (anticipated) | 50 |
|---|
Module description
This module offers the ideal opportunity to develop a deep understanding of data science approaches to your specialism area. There will be supervision from experts in data science and modelling and from experts in your chosen science and technology area. You will apply your technical and application specific skills and knowledge to undertake original and interdisciplinary research within data science and modelling. The project will require understanding of the setting, a critical review of possible approaches, choice of appropriate methodology, an extended piece of data analysis or modelling work and a clear and concise summary of the background, data, methodology, results and conclusions. You will communicate your findings to your peers and for assessment through a dissertation, presentation and other digital media.
Module aims - intentions of the module
This module aims to give you in-depth experience of applying Data Science and Modelling approaches to real-world problems, preparing you for work in a business/industrial/governmental/NGO setting or for further post-graduate study/research. The module aims to build on the knowledge and skills you have acquired in the taught modules of the programme through an investigation of an area of particular interest to you. It aims to give you experience of many aspects of research work, including problem formulation, literature review, planning, tool development, experimentation, analysis, interpretation and presentation of results.
Intended Learning Outcomes (ILOs)
ILO: Module-specific skills
On successfully completing the module you will be able to...
- 1. Demonstrate knowledge of a research topic of relevance to applied data science and modelling, acquired through a deep and self-motivated exploration of that topic
- 2. Design and follow systematically the phases of research project development
- 3. Apply sophisticated and appropriate analysis and development techniques at each stage of a project
ILO: Discipline-specific skills
On successfully completing the module you will be able to...
- 4. Show familiarity with the background and context of an application area
- 5. Apply methods and tools learnt in the context of other fields to the application in question
- 6. Produce full documentation as appropriate to the system and research
ILO: Personal and key skills
On successfully completing the module you will be able to...
- 7. Conduct independent study, including library and web-based research
- 8. Plan an extended project, demonstrate independent research, and manage time effectively
- 9. Present and communicate work to a non-specialist audience
- 10. Technical and scientific report writing and presentation
Syllabus plan
Not applicable.
Learning activities and teaching methods (given in hours of study time)
| Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
|---|---|---|
| 20 | 580 |
Details of learning activities and teaching methods
| Category | Hours of study time | Description |
|---|---|---|
| Scheduled learning and teaching activities | 20 | Project supervision |
| Guided Independent 内射大奶 | 580 | Individual assessed work |
Formative assessment
| Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|
| Two-page project proposal in early stages of project | 2 pages | al | Oral |
| Draft dissertation | 10+ pages | all | Written and/or oral |
Summative assessment (% of credit)
| Coursework | Written exams | Practical exams |
|---|---|---|
| 100 | 0 | 0 |
Details of summative assessment
| Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|---|
| Intermediate reporting | 15 | 2,000 words (or equivalent) | 1-4, 7-10 | Written and oral |
| Final presentation | 15 | 15 minutes | 1, 4, 9 | Written and oral |
| Dissertation | 70 | 15,000 words (or equivalent) | All | Written |
Details of re-assessment (where required by referral or deferral)
| Original form of assessment | Form of re-assessment | ILOs re-assessed | Timescale for re-assessment |
|---|---|---|---|
| Intermediate reporting | Coursework (100%) | 1-4, 7-10 | To be agreed by consequences of failure meeting |
| Final presentation | Coursework (100%) | 1, 4, 9 | To be agreed by consequences of failure meeting |
| Dissertation | Coursework (100%) | All | To be agreed by consequences of failure meeting |
Re-assessment notes
Deferral – if you miss an assessment for certificated reasons judged acceptable by the Mitigation Committee, you will normally be either deferred in the assessment or an extension may be granted. The mark given for a re-assessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.
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Referral – if you have failed the module overall (i.e., a final overall module mark of less than 50%) you will be required to resubmit the original assessment as necessary. The mark given for a re-assessment taken as a result of referral will be capped at 50%.
Indicative learning resources - Basic reading
Basic reading:
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- Subject to project topic
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Web-based and electronic resources:
?
- ELE – College to provide hyperlink to appropriate pages
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Other resources:
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- Subject to project topic
| Credit value | 60 |
|---|---|
| Module ECTS | 30 |
| Module pre-requisites | none |
| Module co-requisites | none |
| NQF level (module) | 7 |
| Available as distance learning? | No |
| Origin date | 01/05/2025 |