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DRPS : Course Catalogue : School of Informatics : Informatics

Undergraduate Course: Honours Project (Data Science Graduate Apprenticeship) (INFR10081)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 10 (Year 4 Undergraduate)
Course typeDissertation AvailabilityNot available to visiting students
SCQF Credits40 ECTS Credits20
SummaryThis is a major project and is intended to allow students to demonstrate their ability to organise and carry out a substantial piece of work. The project involves both the application of skills learnt in the past and the acquisition of new skills. Typical areas of activity will be: gathering and understanding background information; solving conceptual problems; design; implementation; experimentation and evaluation; writing up.

This course is designed for students on the Data Science Apprenticeship, and therefore the project should focus on some aspect of Data Science, whether this may be Data Analytics, Machine Learning, Security and Privacy, Human Factors, Software, Algorithms, Data Visualisation, or another theme of the field of Data Science.

The project will be conducted individually by the student under the joint supervision of a member of teaching staff at the University, and a Mentor at the Industrial Partner which hosts this Graduate Apprentice. We expect the project specification to be designed by the student (as a self-proposed project) with input from the Mentor and proposed supervisor. All project specifications must be approved by the Project Coordinator, and any IP or Ethics concerns must be formally addressed before work on the project begins.
Course description We expect that the student will carry out work on the project during semester 1 of 4th year (while attending University) and complete the work during semester 2 (while on placement with the Industrial host).

The details of the course will be 'Project dependent'.

The project is assessed on the basis of a written report which should typically contain:
- Title page with abstract (a one or two paragraph summary of the contents).
- Introduction and synopsis, in which the project topic is described and set in the context of published literature, and the main results are briefly summarised.
- Discussion of the Industrial collaboration, any extra considerations necessary because of that collaboration, and a discussion of the Data Skills and techniques learned/employed in carrying out the project work.
- Discussion of the work undertaken, in which the various sub-problems, solutions and difficulties are examined.
- If appropriate, a description of experiments undertaken, a presentation of the data gleaned from them, and an interpretation of that data.
- Conclusion, in which the main achievements are reviewed, and unsolved problems and directions for further work are presented.
- Bibliography.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements Only available to students on degree programme (UTBSCDATSC1F)
Course Delivery Information
Academic year 2023/24, Not available to visiting students (SS1) Quota:  None
Course Start Full Year
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 400 ( Lecture Hours 2, Seminar/Tutorial Hours 4, Dissertation/Project Supervision Hours 11, Feedback/Feedforward Hours 1, Summative Assessment Hours 1, Programme Level Learning and Teaching Hours 8, Directed Learning and Independent Learning Hours 373 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) Coursework 100%
Feedback There is only one submission for this course (the dissertation). Students will obtain feedback from the two markers after the exam board. Formative feedback will be provided by the supervisor throughout the year, and from the 'project group organiser' during semester 1.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. structure, summarise and critically evaluate a body of knowledge relating to a substantial project topic in Data Science
  2. conduct a programme of work in further investigation of issues related to this topic
  3. discuss and solve conceptual and/or pragmatic problems which arise during the investigation, and critically evaluate the investigation, with reference to design decisions made.
  4. discuss and evaluate considerations that arise from carrying out a project in collaboration with industry, and evaluate how such an experience can influence development as a Data Scientist.
  5. present the work orally and visually, with demonstration of working artefacts when appropriate
Reading List
Project dependent
Additional Information
Graduate Attributes and Skills This course requires technical ability, independent learning, evaluation and reflection, development of writing and other communication skills, time management, professional awareness. It develops almost all of the Personal and Professional sub-categories mentioned above.
KeywordsProject,Data Science,Graduate Apprenticeship,Dissertation,Independent Project
Course organiserDr Ian Stark
Tel: (0131 6)50 5143
Course secretaryMiss Yesica Marco Azorin
Tel: (0131 6)505113
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