Postgraduate Course: Dissertation (Data science for health and social care) (HEIN11053)
Course Outline
| School | Deanery of Molecular, Genetic and Population Health Sciences |
College | College of Medicine and Veterinary Medicine |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
| Course type | Online Distance Learning |
Availability | Not available to visiting students |
| SCQF Credits | 60 |
ECTS Credits | 30 |
| Summary | The dissertation course is designed to develop students' academic skills and ability to use data science theory to undertake an independent piece of work that forms the final stage of the MSc programme.
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| Course description |
The final stage of the MSc programme culminates in an extended, self-directed piece of work based on a topic in data science in health and social care. The dissertation involves applying skills learnt in the past and the acquisition of new skills.
The dissertation course aims to give students options and an opportunity to further develop their academic practice and apply their learning by crafting a substantial and sustained independent piece of work.
The dissertation topic will be determined on the basis of the student's interests, the expertise of staff, and what is feasible in terms of the literature and time available. Therefore, students are asked to choose a topic that interests them and which has a clear focus and definable boundaries.
The dissertation will assess the students' ability to conduct self-directed research, investigation or development, creatively tackle problems, reflect, critically review to consolidate and extend their knowledge, skills, practices and thinking in a topic and communicate their findings in an articulate and structured approach.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
| Additional Costs | Students will be responsible for their computer equipment and internet access. |
Course Delivery Information
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| Academic year 2022/23, Not available to visiting students (SS1)
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Quota: None |
| Course Start |
Flexible |
| Location |
Activity |
Description |
Weeks |
Monday |
Tuesday |
Wednesday |
Thursday |
Friday |
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| No Classes have been defined for this Course |
| First Class |
First class information not currently available |
| Learning and Teaching activities (Further Info) |
Total Hours:
600
(
Seminar/Tutorial Hours 10,
Dissertation/Project Supervision Hours 10,
Programme Level Learning and Teaching Hours 12,
Directed Learning and Independent Learning Hours
0 )
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| Assessment (Further Info) |
Written Exam
%,
Coursework
%,
Practical Exam
%
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| Additional Information (Assessment) |
Written Exam 0 %, Coursework 100 %, Practical Exam 0 % |
| Feedback |
A balance of formative feedback and feedforward will be provided throughout the course, for example, during live question and answer sessions and on discussion boards. Formative tasks will be offered before the student submit their summative assessed coursework. All components of summative assessment will be marked, and feedback will be provided. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Demonstrate a critical understanding of theories, concepts and principles relating to data-driven innovation in health and/or social care.
- Apply a range of data science skills, theories, practices, and creativity to produce a significant research, investigation, or development project.
- Critically review, consolidate, and extend knowledge, skills, practices, and thinking in data science to extract value from health and social care data.
- Develop innovative responses to problems and issues and communicate data-related issues in the health and social care sector.
- Exercise autonomy and reflexivity to contribute to change, development and/or new thinking within the health and social care sector.
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Reading List
| Dissertation dependent. |
Additional Information
| Graduate Attributes and Skills |
Mindsets:
Enquiry and lifelong learning
Students on this course will be encouraged to seek out ways to develop their expertise in data science in health and social care. They will also be encouraged to strive for excellence in their professional practice and to use established and developed approaches to data-related issues as they arise in health and social care systems.
Aspiration and personal development
Students will be encouraged to draw on the quality, depth and breadth of their experiences to expand their potential and identify areas they wish to develop and grow. Students will also be encouraged to understand their responsibility within and contribute positively, ethically and respectfully to the health and social care community while acknowledging that different students and community members will have other priorities and goals.
Outlook and engagement
Students will be expected to take responsibility for their learning. Students will be asked to use their initiative and experience, often explicitly relating to their professional, educational, geographical or cultural context, to engage with and enhance the learning of students from the diverse communities on the programme. Students will also be asked to reflect on the experience of their peers and identify opportunities to enhance their learning.
Skills:
Research and enquiry
Students will use self-reflection to seek out learning opportunities. Students will also use the newly acquired knowledge and critical assessment to identify and creatively tackle problems and assimilate the findings of primary research and peer knowledge in their arguments, discussions and assessments.
Personal and intellectual autonomy
Students will be encouraged to use their personal and intellectual autonomy to critically evaluate the literature and their learning. Students will be supported through their active participation in self-directed learning, discussion boards and collaborative activities to critically evaluate concepts, evidence and experiences of peers and supervisors from an open-minded and reasoned perspective.
Personal effectiveness
Students will need to be effective and proactive learners that can articulate what they have learned and have an awareness of their strengths and limitations and a commitment to learning and reflection to complete this course successfully.
Communication
Effective data scientists' practitioners in the health and social care sector require excellent oral and written communication, presentation and interpersonal skills. The structure of the dissertation courses will reinforce and develop these skills.
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| Keywords | Not entered |
Contacts
| Course organiser | Dr Mairead Bermingham
Tel:
Email: mairead.bermingham@ed.ac.uk |
Course secretary | Miss Magdalena Mazurczak
Tel:
Email: Magdalena.Mazurczak@ed.ac.uk |
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