Postgraduate Course: MSc by Research Thesis (Data Science) (INFR11106)
Course Outline
| School | School of Informatics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
| Course type | Dissertation |
Availability | Not available to visiting students |
| SCQF Credits | 90 |
ECTS Credits | 45 |
| Summary | Students will pursue an eight-month research project in Data Science which results in a written dissertation. The research must demonstrate competence, knowledge, and be presented in a critical and scholarly way, demonstrating that the student is capable of undertaking independent research. |
| Course description |
Project dependent.
|
Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | For students on the MSc by Research in Data Science only. |
Course Delivery Information
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| Academic year 2014/15, Not available to visiting students (SS1)
|
Quota: None |
| Course Start |
Block 5 (Sem 2) and beyond |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
900
(
Seminar/Tutorial Hours 10,
Dissertation/Project Supervision Hours 25,
Programme Level Learning and Teaching Hours 18,
Directed Learning and Independent Learning Hours
847 )
|
| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
The project is assessed completely on the basis of a written thesis which should typically contain:
Title page with abstract (a one or two paragraph summary of the contents).
Introduction : background, previous work, exposition of relevant literature, setting of the work in the proper context.
Description of the work undertaken : this may be divided into chapters describing the conceptual design work and the actual implementation separately. Any problems or difficulties and the suggested solutions should be mentioned. Alternative solutions and their evaluation should also be included.
Analysis : results and their critical analysis should be reported, whether the results conform to expectations or otherwise and how they compare with other related work.
Conclusion : concluding remarks and observations, unsolved problems, suggestions for further work.
Bibliography.
Students may be required by their project markers to demonstrate any system that arose from the project.
|
| Feedback |
Not entered |
| No Exam Information |
Learning Outcomes
1 - Structure and summarise a body of knowledge relating to a substantial project topic in the area of Data Science.
2 - Critically evaluate previous work in the area.
3 - Conduct a programme of work in further investigation of issues related to the topic.
4 - Discuss and solve conceptual problems which arise during the investigation.
5 - Justify design decisions made during the investigation.
6 - Critically evaluate the investigation.
7 - Present their work, with demonstration of working artifacts where appropriate.
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Reading List
| Project dependent. |
Contacts
| Course organiser | Dr Iain Murray
Tel: (0131 6)51 9078
Email: I.Murray@ed.ac.uk |
Course secretary | Ms Katey Lee
Tel: (0131 6)50 2701
Email: Katey.Lee@ed.ac.uk |
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