Last modified: 23 Jul 2024 11:07
This work-based placement elective offers a professional placement with a civic, government, industrial, public, research or voluntary health and/or development sector organisation in the field of Health Data Science. You will undertake a ten-week placement with your host organisation, either within the organisation, remotely from Aberdeen, or using a combination of both. Placements are subject to availability and are offered on a best match basis.
| Study Type | Postgraduate | Level | 5 |
|---|---|---|---|
| Term | Third Term | Credit Points | 60 credits (30 ECTS credits) |
| Campus | Aberdeen | Sustained Study | No |
| Co-ordinators |
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This is a project-based course, the content and structure of which is largely student-directed. You will contribute work activities for your host organisation (40 hours per week over ten weeks = 400 hours, documented using timesheets) and teaching and learning for this course will also involve a combination of host organisation- and University-facilitated structured education (40 hours, including ten compulsory two-hour University training sessions and work-related training, plus meetings with your placement host and University supervisor). You will also spend time on self-study and preparation for assessments (160 hours).
The aims of this course are to:
Assessments are individually graded, but each of them will build on learning from previous assessments. As a result, a varied portfolio will be developed and assessed to ensure students are given continuous feedback and support to develop high quality project outputs.
Information on contact teaching time is available from the course guide.
| Assessment Type | Summative | Weighting | 30 | |
|---|---|---|---|---|
| Assessment Weeks | 1 | Feedback Weeks | 3 | |
| Feedback |
Executive report on the project outputs and contributions made to the host organization. |
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Procedural | Create | Create appropriate data science outputs and communicate them effectively to the relevant stakeholders. |
| Procedural | Evaluate | Demonstrate evidence of the application of academic and technical skills in the workplace, including collection of relevant data, synthesis, analysis, and interpretation. |
| Reflection | Apply | Demonstrate evidence of the use of open and reproducible science guidelines in technical analysis/outputs. |
| Assessment Type | Summative | Weighting | 20 | |
|---|---|---|---|---|
| Assessment Weeks | 50 | Feedback Weeks | 52 | |
| Feedback |
Post-placement presentation. |
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Procedural | Create | Create appropriate data science outputs and communicate them effectively to the relevant stakeholders. |
| Procedural | Evaluate | Demonstrate evidence of the application of academic and technical skills in the workplace, including collection of relevant data, synthesis, analysis, and interpretation. |
| Reflection | Apply | Demonstrate evidence of the use of open and reproducible science guidelines in technical analysis/outputs. |
| Assessment Type | Summative | Weighting | 10 | |
|---|---|---|---|---|
| Assessment Weeks | 44 | Feedback Weeks | 46 | |
| Feedback |
Roles and responsibilities placement agreement + risk assessment form. Up to 2000 words, including proforma text, tables or references:
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Procedural | Create | Develop work placement roles and responsibilities; and negotiate these with all stakeholders. |
| Procedural | Evaluate | Undertake a risk assessment and prepare a formal agreement with the placement host. |
| Assessment Type | Summative | Weighting | 10 | |
|---|---|---|---|---|
| Assessment Weeks | 45 | Feedback Weeks | 47 | |
| Feedback |
Pre-placement presentation (synchronous) |
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Procedural | Create | Design an efficient project workflow. |
| Assessment Type | Summative | Weighting | 20 | |
|---|---|---|---|---|
| Assessment Weeks | 52 | Feedback Weeks | 1 | |
| Feedback |
GitHub repository showing continued use of open and reproducible science framework. |
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Procedural | Apply | Operate collaborative science platforms. |
| Assessment Type | Summative | Weighting | 10 | |
|---|---|---|---|---|
| Assessment Weeks | 50 | Feedback Weeks | 52 | |
| Feedback |
Post-placement presentation – B. Oral reflective report on skills gained and contributions made during the placement. |
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Reflection | Evaluate | Critically evaluate and describe the work of a health data scientist as it is situated within the broader context of everyday life. |
There are no assessments for this course.
| Assessment Type | Summative | Weighting | 40 | |
|---|---|---|---|---|
| Assessment Weeks | 4 | Feedback Weeks | 6 | |
| Feedback |
The oral examination will last approximately 30 minutes, which will include a presentation of approximately 15 minutes. |
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
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| Assessment Type | Summative | Weighting | 60 | |
|---|---|---|---|---|
| Assessment Weeks | 4 | Feedback Weeks | 6 | |
| Feedback |
The written report will consist on a short health data science project (1500-2000 words), and will require the application of the open and reproducible science framework to address and example healthcare problem. |
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Procedural | Create | Create appropriate data science outputs and communicate them effectively to the relevant stakeholders. |
| Procedural | Create | Develop work placement roles and responsibilities; and negotiate these with all stakeholders. |
| Procedural | Create | Design an efficient project workflow. |
| Reflection | Apply | Demonstrate evidence of the use of open and reproducible science guidelines in technical analysis/outputs. |
| Procedural | Apply | Operate collaborative science platforms. |
| Reflection | Evaluate | Critically evaluate and describe the work of a health data scientist as it is situated within the broader context of everyday life. |
| Procedural | Evaluate | Demonstrate evidence of the application of academic and technical skills in the workplace, including collection of relevant data, synthesis, analysis, and interpretation. |
| Procedural | Evaluate | Undertake a risk assessment and prepare a formal agreement with the placement host. |
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