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DRPS : Course Catalogue : School of Biological Sciences : Postgraduate

Postgraduate Course: Statistics for Computational Biologists (PGBI11120)

Course Outline
SchoolSchool of Biological Sciences CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Postgraduate) AvailabilityNot available to visiting students
SCQF Credits20 ECTS Credits10
SummaryThe course will introduce students to the core basics of statistics on a theoretical and practical level. It will include in-depth coverage of topics relevant to bioinformatics and data science, leaving students well prepared for further study and research in these areas.
Course description The course will start by covering the core basics of statistics; probability theory, probability distributions, parameters, sampling distributions, hypothesis tests, prediction, (maximum) likelihood and Bayesian inference. Commonly encountered modelling approaches (regression, ANOVA) will be introduced within the common framework of generalised linear (mixed) models, followed by sessions on specialised statistical methods for handling biological sequence data and large complex data sets. Lectures will be supplemented by practicals that will teach them how to use the programming language R, small-group tutorials and in-course assignments. Students will be required to complete 3 in-class assignments and an open book exam. Students are also expected to attend relevant graduate-level seminars and journal clubs.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Students MUST NOT also be taking Statistics and Data Analysis (PGBI11003)
Other requirements None
Course Delivery Information
Not being delivered
Learning Outcomes
On completion of this course, the student will be able to:
  1. Students will be able to choose and interpret statistical models that can be applied to many types of data, enabling them to make discoveries in a broad range of areas.
  2. Students will have enhanced competence and skills in bioinformatics.
Reading List
Additional Information
Graduate Attributes and Skills SCQF Level 11, Characteristic 3. Generic Cognitive Skills:
Apply critical analysis, evaluation and synthesis to forefront issues.
Identify, conceptualise and define new and abstract problems and issues.
Develop original responses to problems and issues.
Extend knowledge, skills, practices and thinking in the subject.
Deal with complex issues and make informed judgements in situations in the absence of complete or consistent information.

SCQF Level 11, Characteristic 4. Communication, ICT and Numeracy Skills:
Communicate with peers, more senior colleagues and specialists.
Use ICT applications to support and enhance work at this level and adjust features to suit purpose.
Undertake critical evaluations of a wide range of numerical and graphical data.

SCQF Level 11, Characteristic 5.
Autonomy, Accountability and Working with Others:
Exercise substantial initiative in professional and equivalent activities.
Take responsibility for own work.
Practice in ways which draw on critical reflection on own and others roles and responsibilities.
Manage complex ethical and professional issues and make informed judgements on issues not addressed by current professional and/or ethical codes or practices.
Additional Class Delivery Information Total Hours: 200 (Lecture Hours 30, Seminar/Tutorial Hours 9, Supervised Practical/Workshop/Studio Hours 20, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 137)
KeywordsMSc Bioinformatics
Course organiserDr Jarrod Hadfield
Tel: (0131 6)50 5484
Course secretaryMs Louise Robertson
Tel: (0131 6)50 5988
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