Last modified: 23 Jul 2024 10:43
Nationally and internationally there is recognition of the critical shortage in data-intensive analytic capacity applied to healthcare. This course is an introduction to the field of health data science, with examples of real-life healthcare applications, using the popular data science language R.
| Study Type | Postgraduate | Level | 5 |
|---|---|---|---|
| Term | First Term | Credit Points | 15 credits (7.5 ECTS credits) |
| Campus | Aberdeen | Sustained Study | No |
| Co-ordinators |
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This introductory course will give students from a variety of backgrounds a firm understanding of data science and its application to the health domain. The course will cover how data science is used to address healthcare problems; the role of health data scientists in research and healthcare; current challenges in the field; and the data science workflow using SQL and R (no coding experience is required).
Information on contact teaching time is available from the course guide.
| Assessment Type | Summative | Weighting | 30 | |
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback |
A resource such as a poster, infographic, or slide deck, aimed at the general public, to discuss one of the current challenges in health data science. Feedback will be provided a maximum of 3 working weeks after submission. Students will receive a grade, as well as written feedback on their submission. |
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Conceptual | Analyse | Analyse current challenges in health data science |
| Procedural | Understand | Explain how data science is used to address healthcare problems |
| Assessment Type | Summative | Weighting | 70 | |
|---|---|---|---|---|
| Assessment Weeks | Feedback Weeks | |||
| Feedback |
A report describing the application of the health data science workflow to address an example healthcare problem. Feedback will be provided a maximum of 3 working weeks after submission. Students will receive a grade, as well as written feedback on their submission. |
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Conceptual | Evaluate | Discuss the limitations and assumptions made in health data science projects |
| Procedural | Apply | Apply the data science workflow using R to healthcare problems |
| Procedural | Create | Communicate information from health data in written form |
| Procedural | Understand | Explain how data science is used to address healthcare problems |
There are no assessments for this course.
| Assessment Type | Summative | Weighting | 100 | |
|---|---|---|---|---|
| Assessment Weeks | 25 | Feedback Weeks | 28 | |
| Feedback |
A report that discusses one of the current challenges in health data science, and describes the application of the health data science workflow to address an example healthcare problem |
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Conceptual | Analyse | Analyse current challenges in health data science |
| Procedural | Apply | Apply the data science workflow using R to healthcare problems |
| Procedural | Create | Communicate information from health data in written form |
| Conceptual | Evaluate | Discuss the limitations and assumptions made in health data science projects |
| Procedural | Understand | Explain how data science is used to address healthcare problems |
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