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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2022/2023

Timetable information in the Course Catalogue may be subject to change.

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DRPS : Course Catalogue : Deanery of Molecular, Genetic and Population Health Sciences : Public Health Research

Postgraduate Course: Credits Awarded to Taught Courses [University of Glasgow] Data Science MED5378 (PUHR11107)

Course Outline
SchoolDeanery of Molecular, Genetic and Population Health Sciences CollegeCollege of Medicine and Veterinary Medicine
Credit level (Normal year taken)SCQF Level 11 (Postgraduate)
Course typeOnline Distance Learning AvailabilityNot available to visiting students
SCQF Credits20 ECTS Credits10
SummaryThis 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
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:
  1. Critically discuss the key issues of disclosure control and information governance related to the use of administrative health data for research purposes
  2. Evaluate the theoretical principles of data linkage methods, including an understanding of available sources and limitations of linked data sets
  3. Critically assess possible sources of bias and measurement error in administrative health data
  4. Create and interpret quantitative output after data management, data manipulation and transformation of large linked datasets, including linking datasets with different structures
  5. Evaluate the research methods needed to conceptualise and derive numerators and denominators typically used in the analysis of health data
Reading List
None
Additional Information
Graduate Attributes and Skills Not entered
KeywordsNot entered
Contacts
Course organiserDr Susan Farrington
Tel: (0131) 332 2471
Email: Susan.Farrington@ed.ac.uk
Course secretaryMrs Maree Hardie
Tel:
Email: maree.hardie@ed.ac.uk
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