CSC8332 : Bio-data science (Inactive)
CSC8332 : Bio-data science (Inactive)
- Inactive for Year: 2024/25
- Module Leader(s): Dr Pawel Widera
- Owning School: Computing
- Teaching Location: Newcastle City Campus
Semesters
Your programme is made up of credits, the total differs on programme to programme.
| Semester 2 Credit Value: | 10 |
| ECTS Credits: | 5.0 |
| European Credit Transfer System | |
Pre-requisite
Modules you must have done previously to study this module
| Code | Title |
|---|---|
| CSC8621 | Computing Foundations of Data Science |
Pre Requisite Comment
Basic knowledge of Python is required (CSC8621)
Co-Requisite
Modules you need to take at the same time
Co Requisite Comment
N/A
Aims
Recent developments in the biological sciences and medicine resulted in generation of increasing volumes of biological data. This data is typically noisy and complex which poses a challenge to analytical approaches. This research focused module aims to introduce students to key data science concepts whilst providing a practical, hands-on, task driven experience reinforcing the learning through practice.
Outline Of Syllabus
1. Interactive environments for data science
2. Data handling
3. Data integration and workflows
4. Looking into data through statistics
5. Multi-dimensional data visualisation
6. Learning and classification
7. Neural networks and deep learning
8. Discovery and visualisation of clusters
Learning Outcomes
Intended Knowledge Outcomes
To be able to describe and discuss:
1. The challenges in biological data analysis.
2. Data processing techniques.
3. Data integration and workflows.
4. Effective data visualisation.
5. Basic machine learning approaches (classification, clustering, deep learning with text data).
Intended Skill Outcomes
To be able to:
Clean, shape, and transform biological data.
Visualise data distributions and relationships.
Design data processing and machine learning workflows.
Apply statistical and simple machine intelligence methods to biological data.
Evaluate and interpret machine learning models.
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Guided Independent Study | Assessment preparation and completion | 8 | 0:15 | 2:00 | Online quizzes |
| Structured Guided Learning | Lecture materials | 8 | 0:30 | 4:00 | Key concepts (pre-recorded) |
| Scheduled Learning And Teaching Activities | Practical | 8 | 1:30 | 12:00 | Guided practice sessions (in person) |
| Scheduled Learning And Teaching Activities | Small group teaching | 1 | 1:00 | 1:00 | Tutorial feedback session to support coursework. |
| Guided Independent Study | Skills practice | 7 | 2:00 | 14:00 | Asynchronous practicals |
| Guided Independent Study | Project work | 30 | 1:00 | 30:00 | Coursework and report writing |
| Scheduled Learning And Teaching Activities | Drop-in/surgery | 3 | 1:00 | 3:00 | Support for queries about lecture material or coursework. |
| Guided Independent Study | Independent study | 30 | 1:00 | 30:00 | Background reading |
| Guided Independent Study | Independent study | 8 | 0:30 | 4:00 | Revise lecture materials |
| Total | 100:00 |
Teaching Rationale And Relationship
This is a very practical subject, therefore all the learning material will be supported by hands-on practical sessions. The guided practice will focus on application of the concepts introduced in the lectures to real biological data, and will equip students with practical skills needed in the individual work on the final report.
Lectures will be used to introduce the learning material and to demonstrate the key concepts by example. Students are expected to re-watch the lectures after each practice session to aid deep learning.
Online discussion on Canvas, drop-in sessions / office hours will be used to enhance learning and provide help with the coursework.
Students aiming for 1st class marks are expected to widen their knowledge beyond the taught material through background reading.
Reading Lists
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 | 90 | Practical report on selected data handling and analysis tasks. Max 2,000 words. |
| Computer assessment | 2 | M | 10 | Online quizzes. Short questions testing the understanding of key theoretical and practical concepts after each guided practice session. |
Assessment Rationale And Relationship
The report is the main summative assessment that allows students to apply data science techniques and test their practical skills on different datasets. The regular online quizzes test the understanding of key concepts.
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- CSC8332's Timetable
Past Exam Papers
- Exam Papers Online : www.ncl.ac.uk/exam.papers/
- CSC8332's past Exam Papers
General Notes
N/A
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The information contained within the Module Catalogue relates to the 2024 academic year.
In accordance with University Terms and Conditions, the University makes all reasonable efforts to deliver the modules as described.
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