Postgraduate Course: Dissertation (HPC with Data Science) (INFR11164)
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 | 60 |
ECTS Credits | 30 |
| Summary | The dissertation comprises a report of c.15000 words describing an MSc project undertaken between May and mid-late August applying the practical skills and knowledge from the taught courses on MSc in HPC with Data Science. Students are also required to present their work to the department.
The dissertation must be based on original work carried out solely by the candidate and conform to the University's regulations. The project work may be undertaken within the University or in an external organisation with an appropriate focus. The subject area covered by the project can, with the approval of the Programme Director, be chosen by the candidate to align with their own research interests. |
| Course description |
Research-level work in Data Science, to include:
- Critically review previous work in the area.
- Plan and implement a programme of work to investigate the topic.
- Analyse the results in the context of previous work.
- Present the work in the form of a written dissertation.
- Summarise the work in an oral presentation to an academic audience.
|
Course Delivery Information
|
| Academic year 2020/21, Not available to visiting students (SS1)
|
Quota: 80 |
| Course Start |
Block 5 (Sem 2) and beyond |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
600
(
Dissertation/Project Supervision Hours 12,
Summative Assessment Hours 1,
Programme Level Learning and Teaching Hours 12,
Directed Learning and Independent Learning Hours
575 )
|
| Additional Information (Learning and Teaching) |
External visit hours may be required for some industrial projects.
|
| Assessment (Further Info) |
Written Exam
0 %,
Coursework
92 %,
Practical Exam
8 %
|
| Additional Information (Assessment) |
92% Dissertation and 8% oral presentation. |
| Feedback |
Provided through interaction with supervisor and on assessed work. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Critically review previous work in the area.
- Plan and implement a programme of work to investigate the topic.
- Analyse the results in the context of previous work.
- Present the work in the form of a written dissertation.
- Summarise the work in an oral presentation to an academic audience.
|
Reading List
| Dependent on project. Input provided by supervisor, but primarily self-sourced by student. |
Additional Information
| Graduate Attributes and Skills |
Effective written and diagrammatic communication.
Reflection on learning and practice.
Adaptation to circumstances.
Solution Exploration, Evaluation and Prioritisation. |
| Special Arrangements |
Only available to students on MSc programme in High Performance Computing with Data Science |
| Additional Class Delivery Information |
No scheduled classes. although students must attend their presentation session - usually on the Tuesday or Wednesday of the final full week of August (though students are encouraged to attend the other sessions to support their classmates).
Students are expected to meet with their supervisor(s) on average weekly during the project. The precise meeting schedule will be determined by the requirements of the project and agreed by the student with their supervisor(s).
It is the student's responsibility to remain engaged and arrange meetings with their supervisor(s) and to prepare for meetings to get the most out of these, making use of best practice as set out during the Project Preparation course.
Students are expected plan and schedule meetings with supervisor(s) in advance and should make every effort to keep such meetings unless a change has been agreed with the supervisor. Although many supervisors will be available to answer queries on a more ad-hoc basis this may not always be possible due to other work commitments or leave. |
| Keywords | Dissertation,HPCwDS,EPCC,Research Project,Industrial Project,Parallelism,Data Science |
Contacts
| Course organiser | Dr Anna Roubickova
Tel:
Email: a.roubickova@epcc.ed.ac.uk |
Course secretary | Mr Ben Morse
Tel: (0131 6)51 3398
Email: Ben.Morse@ed.ac.uk |
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