ST421-30 Data Science Masters Dissertation
Introductory description
This module is core for students on the MSci in Data Science. It is not available as an unusual option for other students.
In this module you will carry out a significant project over the course of terms 1-3 on a topic of specialist interest. You will be provided with an opportunity to express preferences for your project during the preceding academic year and will be allocated to a project / supervisor before the start of the academic year.
Module aims
To allow students to integrate, apply and extend knowledge and skills gained in the taught component of their degree and mastery of some element of Data Science. To give students an introduction to research in the discipline.
Outline syllabus
This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.
Students will be allocated a dissertation supervisor in the Departments of Statistics or Computer Science. The module will entail regular supervisory meetings as well as independent study and research on a chosen topic and the writing of the dissertation. The dissertation will be on a topic within data science. Students will be provided with a list of available topics but may suggest their own provided this is agreed with the supervisor.
Learning outcomes
By the end of the module, students should be able to:
- Conduct a literature review on a selected topic;
- Plan and develop an advanced project themed in Data Science;
- Demonstrate an in-depth understanding of an advanced topic or technique within Data Science;
- Understand, apply and evaluate theory and methodology of relevant literature or techniques, showing good judgement in their selection, application and interpretation;
- Communicate the results of advanced scholarship or research to a specialist audience;
- Produce an extended piece of academic writing with appropriate referencing of literature sources.
Indicative reading list
- Zobel, J (2014). Writing for Computer Science. Springer.
- Neville, C. (2010). The complete guide to referencing and avoiding plagiarism. McGraw-Hill Education (UK).
- Mittelbach, F., Goossens, M., Braams, J., Carlisle, D., & Rowley, C. (2004). The LATEX companion. Addison-Wesley Professional.
- Krantz, S. G. (2017). A primer of mathematical writing: Being a disquisition on having your ideas recorded, typeset, published, read, and appreciated (Vol. 243). American Mathematical Soc..
View reading list on Talis Aspire
Research element
plan and develop an advanced project in data science;
Subject specific skills
TBC
Transferable skills
TBC
Teaching split
| Provider | Weighting |
|---|---|
| Computer Science | 50% |
| Statistics | 50% |
Study time
| Type | Required |
|---|---|
| Lectures | 4 sessions of 30 minutes (1%) |
| Project supervision | 10 sessions of 1 hour (3%) |
| Supervised practical classes | 2 sessions of 1 hour (1%) |
| Private study | 186 hours (62%) |
| Assessment | 100 hours (33%) |
| Total | 300 hours |
Private study description
Find, read and evaluate literature, plan and develop research question, collect and find data, carry out analysis, evaluate and interpret findings, communicate results and write dissertation.
Costs
No further costs have been identified for this module.
You do not need to pass all assessment components to pass the module.
Assessment group A1
| Weighting | Study time | Eligible for self-certification | |
|---|---|---|---|
| Workflow Report | 10% | 10 hours | Yes (extension) |
|
Due in Term 1 Week 8. You will product a workflow report to summarise the topic of your dissertation along with the aims and associated steps in order to complete the dissertation. Your report should be minimal in technical detail, and should be at a level of exposition that another student within the same cohort could read and understand. 500 words is equivalent to one page of text, diagrams, formula or equations; your workflow report should not exceed 6 pages in length. |
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| Oral Presentation | 15% | 10 hours | No |
|
You will prepare and deliver a presentation about your dissertation project to a small audience of your peers and academic staff during a scheduled 2 hour session in week 9 or 10 of term 2. Your presentation will last 12 minutes and will be followed by questions from the audience. |
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| Written dissertation | 75% | 80 hours | Yes (extension) |
|
Due in Term 3 Week 2. You will produce a written dissertation on your research project at a level of exposition and technical detail appropriate for another student within the same cohort. 500 words is equivalent to one page of text, diagrams, formula or equations; your dissertation is likely to be between 40-80 pages in length. Up to 10% of the mark awarded for the dissertation will reflect the extent to which you took intellectual ownership of the project; this is an academic judgement on your quality of understanding beyond that evidenced in the written assessment components of the dissertation. |
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Feedback on assessment
- Face-to-face feedback will be given on the workflow report component by Term 1 Week 10 by individual arrangement with supervisor.
- A short feedback report will provided for the presentation within 20 working days.
- Written feedback will be given on the written dissertation following the examination board.
Anti-requisite modules
If you take this module, you cannot also take:
- EC400-30 Statistics Masters Dissertation in Economics
- IB403-30 Dissertation (MMORSE)
- ST415-30 Statistics Masters Dissertation
Courses
This module is Core for:
- Year 4 of USTA-G304 Undergraduate Data Science (MSci)