Postgraduate

Modules

Modules

CSC8332 : Bio-data science

Semesters
Semester 2 Credit Value: 10
ECTS Credits: 5.0

Aims

Recent developments in the biological sciences and medicine are now moving toward the production of increasing volumes of complex, noisy and large biological datasets. New strategies are being developed to handle these data. This research focussed module aims to introduce the students to the theory behind these strategies whilst providing a practical, hands-on, project driven approach to reinforcing this learning through practice.

Outline Of Syllabus

1. Science behind computational analysis of Biological data
2. The biodata lifecycle
3. Data representation, standards, and biological system representation
4. Exploratory biological data analysis
5. Aspects of machine intelligence and experimental design
6. Semantics and knowledge representation
7. Strategies for biological data integration
8. Bioinformatics Services and workflows
9. Biological data sharing, privacy and security

Teaching Methods

Please note that module leaders are reviewing the module teaching and assessment methods for Semester 2 modules, in light of the Covid-19 restrictions. There may also be a few further changes to Semester 1 modules. Final information will be available by the end of August 2020 in for Semester 1 modules and the end of October 2020 for Semester 2 modules.

Teaching Activities
Category Activity Number Length Student Hours Comment
Scheduled Learning And Teaching ActivitiesLecture121:0012:00Lectures
Scheduled Learning And Teaching ActivitiesPractical103:0030:00Practicals
Scheduled Learning And Teaching ActivitiesSmall group teaching241:0024:00Guidance with practical classes and tutorials
Guided Independent StudyProject work161:0016:00coursework 2
Guided Independent StudyProject work81:008:00coursework 1
Guided Independent StudyIndependent study101:0010:00Background reading
Total100:00
Teaching Rationale And Relationship

Lectures will be used to introduce the learning material and for demonstrating the key concepts by example. Students are expected to follow-up lectures by re-reading and annotating lecture notes to aid deep learning.

Tutorials will be used to emphasise the learning material and its application to the solution of problems and exercises set as coursework, during which students will analyse problems as individuals and in teams.

This is a very practical subject and a large amount of the module is dedicated to a practical exercise. It is important that the learning materials are supported by hands-on opportunities provided by practical classes. Students are expected to spend time on coursework outside timetabled practical classes.

Students aiming for 1st class marks are expected to widen their knowledge beyond the content of lecture notes through background reading.

Assessment Methods

Please note that module leaders are reviewing the module teaching and assessment methods for Semester 2 modules, in light of the Covid-19 restrictions. There may also be a few further changes to Semester 1 modules. Final information will be available by the end of August 2020 in for Semester 1 modules and the end of October 2020 for Semester 2 modules.

The format of resits will be determined by the Board of Examiners

Other Assessment
Description Semester When Set Percentage Comment
Report2M100Summative assessment Coursework 2: A practical report on a data handling and analysis exercise. Max 2,000 words.
Formative Assessments
Description Semester When Set Comment
Essay2MCoursework1: Pass/fail formative assessment on data representation for coursework 2. Max 500 words
Assessment Rationale And Relationship

The formative assessment, coursework1, will assess the students’ ability to understand the concepts of a range of a biological data representation strategies as the module progresses and provide feedback on their approach to coursework2. The summative project exercise, coursework2, will assess the students’ final ability to apply the concepts learned to the development of new data representation, integration and analysis strategies.

Reading Lists

Timetable