DISSERTATION - 2022/3
Module code: MATM064
Module Overview
The dissertation consists of a written report of around 50 pages completed by the student towards the end of their programme of study. The report is based on a major piece of work that involves applying material encountered in the taught component of the programme and extending that knowledge with the student's contribution, under the guidance of a supervisor. The work for the dissertation and the writing up begins approximately May/June, continues through the Summer and the dissertation report is submitted in late Summer. The work may, but need not, involve original research. It may instead consist of a substantial literature survey on a specific topic.
Module provider
Mathematics & Physics
Module Leader
BRODY Dorje (Maths & Phys)
Number of Credits: 60
ECTS Credits: 30
Framework: FHEQ Level 7
Module cap (Maximum number of students): N/A
Overall student workload
Independent Learning Hours: 586
Seminar Hours: 12
Captured Content: 2
Module Availability
Semester 2
Prerequisites / Co-requisites
N/A
Module content
The dissertation is the result of an expected 600 hours of work. Most of this is done individually by the student, in locating and reading relevant sources, working on the technical contribution that is the main part of the dissertation, and writing up the final report. Some time is also spent in regular discussions with the supervisor. Further details are given in the programme handbook.
Assessment pattern
| Assessment type | Unit of assessment | Weighting |
|---|---|---|
| Project (Group/Individual/Dissertation) | Individual research project | 80 |
| Oral exam or presentation | Oral presentation | 20 |
Alternative Assessment
N/A
Assessment Strategy
The assessment strategy is designed to provide students with the opportunity to demonstrate:
- Their ability to independently research and report upon a mathematical topic relevant to their degree programme.
- Their ability to prepare and present mathematical work in both a written and oral fashion.
The literature study required as a prerequisite for any research project will broaden their resourcefulness, while the need to cast their findings and work through an oral presentation will enhance both their resilience and employability.
Thus, the summative assessment for this module consists of:
- A written report worth 80% of the module mark.
- A viva voce examination, after the submission of the report; worth 20% of the module mark.
Formative assessment and feedback
Students receive continuous feedback through regular meetings with their supervisor during the period of the dissertation.
Module aims
- To provide an opportunity for students to pursue a single topic in depth and to demonstrate evidence of research ability at a Masters level. The topic would normally be related to current or recent research within the broad area of mathematical data science. Students are encouraged to either carry out an original piece of mathematical work or carry out a substantial survey of the literature on a particular topic.
Learning outcomes
| Attributes Developed | ||
| 001 | Develop the skill to use research databases such as arXiv, Google Scholar, or MathSciNet. | KPT |
| 002 | Ability to select and interpret sources relevant to the topic. | KC |
| 003 | Acquire an advanced level of specialized mathematical knowledge and understanding in the field of study. | KC |
| 004 | Demonstrate the ability to build upon the concepts, theories, and knowledge gained in the taught component of the MSc programme. | KC |
| 005 | Ability to demonstrate their command of the subject matter of their dissertation via a written report, as well as verbally via an oral presentation. | KPT |
| 006 | Demonstrate independent, critical, and analytical skills, and to evaluate evidence. | KCPT |
Attributes Developed
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Methods of Teaching / Learning
Regular meetings with the supervisor to discuss progress with the dissertation and report writing.
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.
Reading list
https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: MATM064
Other information
The dissertation is very likely to involve practical implementation of data analytics using Python or other programming language. As such, students are expected to enhance their digital capabilities through practical implementation. The knowledge gained through research will strengthen their resourcefulness and resilience via exposure to cutting-edge research ideas as well as learning techniques for solving problems; while through literature review, exposing them potentially to some deep thinking by internationally established leading researchers, their global and cultural intelligence will be enhanced. Through drafting the dissertation and oral presentation, the students will improve their overall presentational skills, which in turn will contribute toward improving their employability. Depending on the choice of the project there are scopes for engaging with sustainability.
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2022/3 academic year.