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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2022/2023

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DRPS : Course Catalogue : School of Informatics : Informatics

Undergraduate Course: Informatics Project Proposal (Graduate Apprenticeship) (INFR10082)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 10 (Year 4 Undergraduate) AvailabilityNot available to visiting students
SCQF Credits20 ECTS Credits10
SummaryThe aim of this course is to develop generic research and/or practical skills that can be deployed in academic or commercial environments. Apprentices will demonstrate their ability to develop interesting concepts and/or hypotheses into proposals appropriate for larger research- or implementation-based project and demonstrate their ability to identify legal, social, ethical and professional issues.
Course description The structure and delivery of this course will vary according to the nature of the project and will be agreed upon between student and supervisor at the start of the semester. Representative activities might include literature review, data preparation, preliminary implementation, or establishing connections and gathering requirements from stakeholders. The student will then produce a project proposal that explains the hypotheses and/or goals, project management, and milestones with approximate times with some justification for these decisions. Key methodologies should be introduced.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements The course is only available to students on the Data Science (Graduate Apprenticeship) programme.
Course Delivery Information
Academic year 2022/23, Not available to visiting students (SS1) Quota:  None
Course Start Semester 1
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Lecture Hours 4, Seminar/Tutorial Hours 6, Feedback/Feedforward Hours 6, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 180 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) Written Exam 0%
Practical Exam 0% (for courses with programming exams)
Coursework 100%

The assessment will consist of:
1. 20% Coursework 1: A review of a project proposal (an IPP exemplar) discussing its strengths and weaknesses
2. 80% Coursework 2: A full project proposal, including background, motivation, and a description of the methodology and expected outcomes.
A good proposal might be organised as follows:
- Purpose: a statement of the problem to be addressed.
- Background: a short description of how previous work addresses (or fails to address) this problem.
- Research: Literature review of relevant research to the project.
- Methods: A description of the methods and techniques to be used, indicating that alternatives have been considered and ruled out on sound scientific or engineering grounds.
- Evaluation: Details of the metrics or other methods by which the outcomes will be evaluated.
- Consideration of legal, social, ethical or professional issues particular to the project.
- Workplan: A timetable detailing what will be done to complete the proposed project, and when these tasks will be completed.
- Professional development: A short outline of how this project will contribute to their professional development as a data scientist.
Feedback Both pieces of coursework will us a detailed rubric grid that will provide consistent feedback across IPP(GA) submissions. In addition, points of improvement will be identified.
No Exam Information
Learning Outcomes
On completion of this course, the student will be able to:
  1. Critically evaluate research literature or other prior work appropriate for their project subject.
  2. Use existing research literature or other prior work to justify choices in experimental design, theoretical goals, and/or implementation.
  3. Develop a structured project proposal.
  4. Outline project/research management issues and potential legal, social, ethical or professional issues.
  5. Illustrate how the proposed project is linked to their professional development as a data science practitioner under the Graduate Apprenticeship scheme.
Reading List
None
Additional Information
Graduate Attributes and Skills 1. Cognitive skills:
- analytical thinking in constructing a critical review of literature relating to the proposed project.
- handling ambiguity in developing the proposed work that complements and develops on existing work. There are many possible proposals and the student must work with multiple potential projects to arrive at the final proposal.

2. Responsibility, autonomy, effectiveness:
- independent learning - apprentices will work independently on developing their proposals and linking them to their work context.
- self-awareness and reflection - apprentices will reflect on their working environment and link the proposal to that environment.
- creativity - the proposal will be a creative synergy of the ideas for the project with the work context the apprentice has and will be experiencing.
- organization and time management - the proposal will involve planning and allocation of time and other resources to ensure completion.
- ethical/social/professional awareness and responsibility - the proposal will require taking account of the professional context the apprentice is working in.

3. Communication:
- verbal and/or written communication - the proposal will require a range of writing skills including communication to the non-specialist.
- cross-disciplinary communication - it is likely that the project will have cross-disciplinary aspects because the apprentice will be expected to link to their wider professional development.
Special Arrangements Only available to students on the Data Science (Graduate Apprenticeship) programme.
KeywordsApprenticeship,proposal,transferrable-skills,planning,risk-analysis
Contacts
Course organiserDr Heather Yorston
Tel:
Email: Heather.Yorston@ed.ac.uk
Course secretaryMiss Lori Anderson
Tel: (0131 6)51 4164
Email: lori.anderson@ed.ac.uk
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