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CSC8332 : Bio-data science (Inactive)

  • Inactive for Year: 2024/25
  • Module Leader(s): Dr Pawel Widera
  • Owning School: Computing
  • Teaching Location: Newcastle City Campus

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


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

Teaching Methods

Teaching Activities
Category Activity Number Length Student Hours Comment
Guided Independent StudyAssessment preparation and completion80:152:00Online quizzes
Structured Guided LearningLecture materials80:304:00Key concepts (pre-recorded)
Scheduled Learning And Teaching ActivitiesPractical81:3012:00Guided practice sessions (in person)
Guided Independent StudySkills practice72:0014:00Asynchronous practicals
Scheduled Learning And Teaching ActivitiesSmall group teaching11:001:00Tutorial feedback session to support coursework.
Guided Independent StudyProject work301:0030:00Coursework and report writing
Scheduled Learning And Teaching ActivitiesDrop-in/surgery31:003:00Support for queries about lecture material or coursework.
Guided Independent StudyIndependent study80:304:00Revise lecture materials
Guided Independent StudyIndependent study301:0030:00Background reading
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.

Assessment Methods

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

Other Assessment
Description Semester When Set Percentage Comment
Report2M90Practical report on selected data handling and analysis tasks. Max 2,000 words.
Computer assessment2M10Online 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.

Reading Lists