DSC8103 : Industrial Biosciences Project Module
- Offered for Year: 2025/26
- Available to incoming Study Abroad and Exchange students
- Module Leader(s): Dr Matt Bawn
- Owning School: Natural and Environmental Sciences
- Teaching Location: Newcastle City Campus
Semesters
Your programme is made up of credits, the total differs on programme to programme.
| Semester 3 Credit Value: | 60 |
| ECTS Credits: | 30.0 |
| European Credit Transfer System | |
Pre-requisite
Modules you must have done previously to study this module
Pre Requisite Comment
Module 1 and Module 2
Co-Requisite
Modules you need to take at the same time
Co Requisite Comment
N/A
Aims
The Biosciences Capstone Project is delivered through a structured, three-part dissertation module that reflects the full research and development cycle commonly encountered in industrial bioscience and biotechnology. It is designed to consolidate and extend students’ abilities in data sourcing, cleaning, analysis, modelling, and interpretation, while fostering both independent critical thinking and collaborative problem-solving in real-world contexts.
In Part 1, students critically engage with current bioscience literature, dissecting how data is acquired, processed, and interpreted. They evaluate the design, reproducibility, and transparency of published workflows, developing a strong foundation in analytical thinking and scientific rigour. In Part 2, each student undertakes an individual data science project based on a shared group-defined research theme and dataset. This approach balances individual autonomy with team-based continuity, enabling students to personalise their investigative focus while contributing to a broader applied research narrative. In Part 3, students return to collaborative work in a team-based, time-constrained hackathon, where they apply agile methods to solve a practical industry-inspired challenge, simulating fast-paced innovation environments.
Across all three parts, students engage with authentic datasets and tools commonly used in industry, including R, Python, Jupyter, and R Markdown. The module places strong emphasis on reproducibility, data ethics, communication, and the integration of domain-specific biological knowledge with computational methods. By the end of the capstone experience, students will have developed a coherent body of applied work that showcases their readiness for data-driven roles in bioscience research, development, and innovation.
Outline Of Syllabus
• Critical review of data-driven bioscience literature, including evaluation of data workflows, reproducibility, and methodological design
• Development of research questions and project planning based on group-defined industrial or applied bioscience themes
• Independent data analysis and modelling using appropriate statistical or machine learning techniques in R or Python
• Workflow documentation, reproducibility, and ethical data handling in life science contexts
• Communication of results through written reports, presentations, and peer feedback
• Team-based problem-solving in a timed hackathon setting using agile and collaborative approaches
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Guided Independent Study | Assessment preparation and completion | 1 | 140:00 | 140:00 | Report based on groupwork - Assessment and Preparation |
| Guided Independent Study | Assessment preparation and completion | 1 | 100:00 | 100:00 | Assessment - Part 1 flowchart |
| Guided Independent Study | Assessment preparation and completion | 1 | 100:00 | 100:00 | Group Hackathon - guided hypothesis testing on a biological dataset |
| Guided Independent Study | Directed research and reading | 1 | 239:00 | 239:00 | Writing up notes, reading on topics of interest and independent research |
| Scheduled Learning And Teaching Activities | Workshops | 8 | 1:00 | 8:00 | Part 2 workshop - Synchronous in-person |
| Scheduled Learning And Teaching Activities | Workshops | 3 | 1:00 | 3:00 | Part 1 workshop |
| Scheduled Learning And Teaching Activities | Drop-in/surgery | 10 | 1:00 | 10:00 | Online drop in |
| Total | 600:00 |
Teaching Rationale And Relationship
This module delivers content through integrated computational workshops, where short lecture segments are interwoven with hands-on modelling exercises using R and Python. This approach ensures students immediately apply statistical and machine learning concepts to real datasets, directly supporting both knowledge and skills outcomes.
Workshops are structured around authentic bioscience scenarios, with a focus on hypothesis framing, model evaluation, and biological interpretation. Weekly practical tasks and group activities enable students to build confidence and fluency with computational tools such (e.g. DESeq2, scikit-learn, and Prophet).
To enhance industry relevance, asynchronous industrial talks are included, offering insight into how data modelling is used in fields such as fermentation optimisation, bioreactor monitoring, and antimicrobial resistance prediction. These talks help contextualise technical skills within real-world decision-making and innovation.
The combination of applied instruction, industry engagement, and collaborative activities ensures that students develop both the conceptual understanding and technical competencies required for data-led roles in industrial biosciences.
Assessment Methods
The format of resits will be determined by the Board of Examiners
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Written exercise | 3 | M | 20 | Part 1 flowchart - Flow chart depicting workflow necessary to generate paper figure. |
| Report | 3 | M | 50 | Report based on groupwork - Max. 20 A4 pages. Full modelling pipeline with biological interpretation (e.g., forecasting CHO bioreactor yield, predicting resistance phenotypes) |
| Practical/lab report | 1 | M | 30 | Group Hackathon Guided hypothesis testing on a biological dataset (e.g., transcriptomics, metagenomics, AMR) |
Formative Assessments
Formative Assessment is an assessment which develops your skills in being assessed, allows for you to receive feedback, and prepares you for being assessed. However, it does not count to your final mark.
| Description | Semester | When Set | Comment |
|---|---|---|---|
| Portfolio | 3 | M | Formative feedback opportunities will be available throughout modules during workshops and activities. |
Assessment Rationale And Relationship
This module is delivered through a scaffolded, project-based format that mirrors the structure and pace of real-world bioscience research. Rather than traditional lectures, learning is driven by independent and collaborative inquiry, supported by supervision meetings, structured workshops, and peer feedback sessions.
The first stage of the module is designed to create a clear and supportive space for students to become familiar with what is expected in the capstone project. By engaging with published data workflows and deconstructing real-world research examples, students develop a strong understanding of the standards, structure, and scope of high-quality data science work in industrial bioscience contexts. This foundation builds the confidence and critical perspective needed to define meaningful project questions and design reproducible analytical workflows.
As the module progresses, learning becomes increasingly student-led. In the second stage, students undertake individual projects based on group-defined themes, applying statistical, machine learning, and domain-specific techniques (e.g. DESeq2, scikit-learn, Prophet) to authentic biological or environmental datasets. Tools such as R, Python, Jupyter, and R Markdown are used to support reproducible analysis and professional reporting.
The final stage culminates in a live, time-limited hackathon co-delivered with industry partners, providing an authentic setting where students apply agile, collaborative problem-solving to a real-world challenge. Industry participation ensures students engage directly with external stakeholders and current sector needs, strengthening the relevance and impact of the experience.
This combination of scaffolded inquiry, independent research, and industry-facing collaboration ensures that students graduate with advanced technical expertise, project delivery experience, and the confidence to contribute meaningfully to data-driven bioscience innovation.
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- DSC8103's Timetable