Postgraduate Course: Introduction to Biomedical Data Science (MCLM11087)
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) |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | In this course, students will be introduced to Biomedical Data Science by receiving a series of lecture-based teaching, training and workshops, to provide a grounding in analysis of biomedical data |
| Course description |
Teaching in the form of lectures, training and workshops will give in-depth understanding of the biomedical data analysis.
Teaching will be delivered in small student groups with considerable opportunities for discussion, exploration and development of data analytical skills including use of R, python, unix and supercomputing.
Students will perform a project, using the command line on eddie to analyse a small sequencing dataset through QC, alignment & either quantification, or variant calling dependent on RNA-seq or DNA-seq datasets, performing some summary statistics in R, visualization with IGV, and writing a simple script in Python. The students would be expected to use github or the University gitlab server to store their work and results.
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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 2024/25, Not available to visiting students (SS1)
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Quota: 30 |
| Course Start |
Flexible |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 70,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
28 )
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| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
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| Additional Information (Assessment) |
100% - analytical project |
| Feedback |
Students will be given written feedback on their project. |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- demonstrate critical understanding of data science analytical skills and engage in dialogue with about their interdisciplinary research
- critically discuss and design good research practice for scientific computing
- effectively plan, organise and manage their analyses
- critically evaluate and communicate the impact of these analyses
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Additional Information
| Graduate Attributes and Skills |
Provide details of the Graduate Attributes and Skills provided by the course
This course will enable the students to develop a wide range of Graduate Attributes and Skills that will contribute to their professional growth as successful researchers, data scientists and experts in their field.
- Students will use skilled communication to enhance their understanding of the analytical challenges and to engage effectively with others.
- Students will become innovative, confident, and reflective lifelong learners, developing key analytical skills.
- Students will use their personal and intellectual autonomy to critically evaluate the data from an open-minded and reasoned perspective.
- Students will become effective and proactive individuals, skilled in the ability to identify and creatively tackle problems, influencing positively and adapting to new situations with sensitivity and integrity. |
| Keywords | biomedical science,analysis,good practice,medical impact |
Contacts
| Course organiser | Dr Susan Farrington
Tel: (0131) 332 2471
Email: |
Course secretary | Miss Kate Hardman
Tel: (0131 6)51 7891
Email: |
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