CSC8332 : Bio-data science
- Offered for Year: 2022/23
- Module Leader(s): Dr Pawel Widera
- Owning School: Computing
- Teaching Location: Newcastle City Campus
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
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Structured Guided Learning | Lecture materials | 16 | 0:30 | 8:00 | Revise lecture materials |
| Scheduled Learning And Teaching Activities | Lecture | 16 | 1:00 | 16:00 | PiP lectures |
| Guided Independent Study | Skills practice | 4 | 4:00 | 16:00 | Asynchronous practicals |
| Guided Independent Study | Project work | 24 | 1:00 | 24:00 | coursework |
| Scheduled Learning And Teaching Activities | Drop-in/surgery | 1 | 3:00 | 3:00 | PiP tutorial feedback session to support summative assessment C/W 1 |
| Scheduled Learning And Teaching Activities | Drop-in/surgery | 3 | 1:00 | 3:00 | PiP to support queries about lecture material or practicals |
| Guided Independent Study | Independent study | 30 | 1:00 | 30:00 | Background reading |
| Total | 100:00 |
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 within a few days by re-reading and annotating lecture notes to aid deep learning.
Online discussion, both using canvas and in drop-in surgeries will be used to emphasise the learning material and its application to the solution of problems and exercises set as coursework.
This is a very practical subject, and it is important that the learning materials are supported by hands-on opportunities provided by 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
The format of resits will be determined by the Board of Examiners
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Report | 2 | M | 100 | Summative assessment Coursework: A practical report on a data handling and analysis exercise. Max 2,000 words. |
Formative Assessments
| Description | Semester | When Set | Comment |
|---|---|---|---|
| Report | 2 | M | Formative assessment: report on a small data science exercise. Max 500 words. |
Assessment Rationale And Relationship
The summative project exercise (coursework) 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
- Timetable Website: www.ncl.ac.uk/timetable/
- CSC8332's Timetable