Postgraduate Course: Bioinformatics and Data Management (MCLM11008)

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	10	ECTS Credits	5
Summary	Bioinformatics is at the interface between two of the most influential scientific fields. An appreciation of computational and biological sciences, in particular the terminology employed in both fields, is essential for those working at such an interface. In this course, we aim to cover the following: ż The concepts of computer science that relate to problems in biological sciences ż Commercial and academic perspectives on bioinformatics ż The impact of bioinformatics on the methodologies used in biological science ż The influence biological science has on computing science
Course description	Not entered

Entry Requirements (not applicable to Visiting Students)
Pre-requisites		Co-requisites
Prohibited Combinations		Other requirements	None

Course Delivery Information

Academic year 2017/18, Not available to visiting students (SS1)		Quota: None
Course Start	Semester 2
Timetable	Timetable
Learning and Teaching activities (Further Info)	Total Hours: 100 ( Seminar/Tutorial Hours 5, Online Activities 100, Feedback/Feedforward Hours 40, Formative Assessment Hours 12, Summative Assessment Hours 10, Revision Session Hours 8, Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 0 )
Assessment (Further Info)	Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment)	Online participation: 25% Individual project: 25% Reflective portfolio: 30% Case scenarios: 20%
Feedback	Not entered
No Exam Information

Learning Outcomes
On completion of this course, the student will be able to: Describe the main computational algorithms used in the analysis of biological sequences Discuss the practical limitations of sequence analysis methods and contrast the methods available Appraise common biological data sources and the key contributing error/noise sources in such data Recognise experimental design in biology as critical to subsequent data analysis and representation in bioinformatics Critically evaluate research literature in the field