Postgraduate Course: Credits Awarded to Taught Courses [University of Glasgow] Data Science MED5378 (PUHR11107)
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 | 20 |
ECTS Credits | 10 |
| Summary | This is a placeholder course, designed to record marks for the University of Glasgow part of the programme, PRPHDISPME1F: Precision Medicine (PhD with Integrated Study) |
| Course description |
Please see [University of Glasgow] Data Science - Identifying, Combining and Analysing Health Data Sets MED5378
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
|
| Academic year 2022/23, Not available to visiting students (SS1)
|
Quota: None |
| Course Start |
Flexible |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
200
(
Lecture Hours 10,
Seminar/Tutorial Hours 20,
Programme Level Learning and Teaching Hours 4,
Directed Learning and Independent Learning Hours
166 )
|
| Assessment (Further Info) |
Written Exam
0 %,
Coursework
100 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Students must submit at least 75% by weight of the components (including examinations) of the course's summative assessment. |
| Feedback |
Not entered |
| No Exam Information |
Learning Outcomes
On completion of this course, the student will be able to:
- Critically discuss the key issues of disclosure control and information governance related to the use of administrative health data for research purposes
- Evaluate the theoretical principles of data linkage methods, including an understanding of available sources and limitations of linked data sets
- Critically assess possible sources of bias and measurement error in administrative health data
- Create and interpret quantitative output after data management, data manipulation and transformation of large linked datasets, including linking datasets with different structures
- Evaluate the research methods needed to conceptualise and derive numerators and denominators typically used in the analysis of health data
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Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | Not entered |
Contacts
| Course organiser | Dr Susan Farrington
Tel: (0131) 332 2471
Email: Susan.Farrington@ed.ac.uk |
Course secretary | Mrs Maree Hardie
Tel:
Email: maree.hardie@ed.ac.uk |
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