THE UNIVERSITY of EDINBURGH

DEGREE REGULATIONS & PROGRAMMES OF STUDY 2018/2019

University Homepage
DRPS Homepage
DRPS Search
DRPS Contact
DRPS : Course Catalogue : School of Informatics : Informatics

Postgraduate Course: Dissertation (DSTI - EPCC) (INFR11185)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Postgraduate)
Course typeOnline Distance Learning AvailabilityNot available to visiting students
SCQF Credits60 ECTS Credits30
SummaryThis is a major piece of independent work which forms the final stage of the MSc programme. It is intended to allow students to demonstrate their ability to organise and carry out a substantial investigation into a problem in data science, according to sound scientific principles. The project involves both the application of skills learnt in the past and the acquisition of new skills. The final submission will be expected to be at a level appropriate for an independent researcher and be a good indication of a student┐s potential to go on to be a productive researcher in a relevant sub-discipline of Data Science.

This course is only available to students on the Data Science, technology and Innovation Online Learning MSc.
Course description The project will be supervised by a member of academic staff from EPCC and possibly a co-supervisor appropriate to the context of the research (who may be within another part of the University or may be from an appropriate external organisation).

In this dissertation course you will be working independently on an extended piece of writing which is original and presents new research within it in the form of a sustained argument. The dissertation marks the final stage of your Masters degree and demonstrates that over the course of the programme you have gained the skills and knowledge required to engage in the formal and rigorous process of research. This process entails, but is not restricted to, identifying a suitable research topic, formulating research objectives, organising/analysing data, organising and reviewing relevant literature, devising an appropriate research methodology, reporting results, drawing conclusions and possibly even making relevant recommendations to the wider research community.

The design and conduct of the project will require a high level of commitment and application from the student. The dissertation demonstrates their ability to think scientifically and complete a research report that follows expected academic conventions of style, tone, structuring and referencing. Supervisory support will be supplemented by the detailed project handbook given to all students.

The types of activity involved in each project will vary but will include most of the following:
- researching the literature and gathering background information
- analysing requirements, comparing alternatives and specifying a solution
- analysing and extending relevant theory in novel ways
- designing and implementing the solution
- experimenting with and evaluating the solution
- discussing existing results and presenting new research
- developing written and oral presentation skills

Communication with supervisors, including discussion progress and review of draft materials, will be determined by the student and supervisor and is most likely to be carried out by a combination of email and telephony, although all parties are encouraged to use a wiki which will be set up for each student┐s project. The wiki allows greater version control of submitted work and clearer documentation of discussions/comments than do email or telephone.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Students MUST have passed: Practical Introduction to Data Science (INFR11183)
It is RECOMMENDED that students have passed Practical Introduction to High Performance Computing (INFR11184)
Co-requisites
Prohibited Combinations Other requirements Ability to programme in C/C++, Fortran, Python, or Java.
Course Delivery Information
Academic year 2018/19, Not available to visiting students (SS1) Quota:  None
Course Start Flexible
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 600 ( Dissertation/Project Supervision Hours 12, Programme Level Learning and Teaching Hours 12, Directed Learning and Independent Learning Hours 576 )
Assessment (Further Info) Written Exam 0 %, Coursework 92 %, Practical Exam 8 %
Additional Information (Assessment) Coursework: 92%
Presentation: 8%
Feedback Provided through discussion and meetings (conducted online/by phone) with supervisor, comments on draft work, and on final dissertation
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Structure and summarise a body of knowledge relating to a substantial project topic in 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; justify design decisions made during the investigation
  5. Critically evaluate the investigation; present their work, with demonstration of working artifacts where appropriate
Reading List
Project dependent
Additional Information
Graduate Attributes and Skills Within the work to be undertaken, this course will provide the MSc candidate with the opportunity to develop or further develop the following key graduate attributes:

- in-depth knowledge of specialist discipline
- develop new understanding by exercising critical judgment and challenging knowledge
- be a self-directed and curious learner
- solve problems effectively taking ethical, professional and environmental issues into account
- use information responsibly in a range of contexts
- engage in reflective practice and self-development
- collaborate with others, capitalising on their different thinking, experience and skills
- communicate (written, oral, online) effectively respectful of social and cultural diversity
- application of numeracy
- application of IT
Special Arrangements Only available to students on the DSTI Online Learning MSc programme.
KeywordsResearch project,dissertation,DSTI,Data Science,Programming,industry project,EPCC,Online
Contacts
Course organiserDr Adam Carter
Tel: (0131 6)50 6009
Email: A.carter@epcc.ed.ac.uk
Course secretaryMr Ben Morse
Tel: (0131 6)51 3398
Email: Ben.Morse@ed.ac.uk
Navigation
Help & Information
Home
Introduction
Glossary
Search DPTs and Courses
Regulations
Regulations
Degree Programmes
Introduction
Browse DPTs
Courses
Introduction
Humanities and Social Science
Science and Engineering
Medicine and Veterinary Medicine
Other Information
Combined Course Timetable
Prospectuses
Important Information