Undergraduate Course: Data Science for Health and Biomedical Sciences (BIME10076)
Course Outline
| School | Deanery of Biomedical Sciences |
College | College of Medicine and Veterinary Medicine |
| Credit level (Normal year taken) | SCQF Level 10 (Year 4 Undergraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 20 |
ECTS Credits | 10 |
| Summary | Data science is revolutionising how medicine is understood, how biomedical research is conducted and how healthcare is delivered. Despite the widely-recognised opportunities that data can bring to biomedicine and healthcare, there is a shortage of data skills in the healthcare sector. This course aims to equip honours students with the key foundations and data skills that are needed for data-driven innovation. It provides an introduction to key concepts, principles and methods of data science in health and biomedical research, enabling students to explore the potential for data to transform healthcare. Students will learn how to use current data science tools to process healthcare data for effective analysis and reporting, and gain practical experience in working with data. They will also gain critical understandings of ethical and legal implications of working with healthcare and biomedical data. |
| Course description |
The course aims to provide a broad introduction to data science in health and biomedical sciences, covering key concepts and principles, data analysis skills and implications of working with biomedical and healthcare data. Key topics in the course include: types of human health data; computational methods (e.g. process modelling and machine learning); data wrangling, analysis and reporting using the R programming language; legal considerations and bias in health data. This course is delivered in a flipped classroom format: it is based around short pre-recorded videos, which are complemented with readings and self-guided programming tasks. The students also have a weekly in-person tutorial, which provides an opportunity for further improvement of their programming practice and discussion of the core concepts. We will also offer weekly quizzes to provide the students with valuable formative feedback.
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
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Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
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| Academic year 2022/23, Not available to visiting students (SS1)
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Quota: 25 |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 10,
Supervised Practical/Workshop/Studio Hours 20,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
166 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
100% ICA:
Programming assignment (60%)
Essay-style assignment (40%)
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| Feedback |
Students will be invited to complete a weekly formative quiz, and will be provided feedback on this.
Students will also be given an opportunity to submit a draft of one section of the assessment (data visualisation), and will receive tutor feedback on this work.
Finally, tutorial classes will be structured to allow students to ask questions, and gain feedback on the data interpretation skills that they will develop throughout the course.
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| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Apply a range of specialised data science techniques to different medical and healthcare scenarios.
- Analyse health and biomedical data with the use of the R programming language, including summarisation, visualisation and interpretation.
- Critically examine the ethical, societal and regulatory principles and implications of data science in health.
- Explain and critically discuss key concepts, principles and methods of data science in health.
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Reading List
There is no compulsory course text.
Recommended materials include:
R for Health Data Science by Ewen Harrison and Riinu Pius
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Additional Information
| Graduate Attributes and Skills |
Digital literacy and numeracy, including using advanced data analysis tools to support their research and enquiry.
¿ Critical and analytical thinking, including applying critical analysis, synthesis and evaluation to key approaches and development in the subject.
¿ Communication, including communicating complex ideas and arguments to a range of audiences with different levels of knowledge/expertise.
¿ Personal and intellectual autonomy, including planning organising work, time management and taking responsibility for own work.
¿ Employability, including key data science skills that are in high demand among employers globally
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| Keywords | Data Science,Healthcare,Human Health Data,Biomedical data,R programming,Ethics |
Contacts
| Course organiser | Dr Kasia Banas
Tel:
Email: |
Course secretary | |
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