Coding for Biomedical Scientists
| Module title | Coding for Biomedical Scientists |
|---|---|
| Module code | CSC2030 |
| Academic year | 2026/7 |
| Credits | 15 |
| Module staff | Dr Federico Palmisani (Convenor) |
| Duration: Term | 1 | 2 | 3 |
|---|---|---|---|
| Duration: Weeks | 12 |
| Number students taking module (anticipated) | 80 |
|---|
Module description
In this practical module, we will introduce you to Matlab, a programme commonly used among the biomedical scientists and neuroscientists who are invested in research for carrying out analyses of complex data sets. Once you have grasped basic Matlab skills, you will learn how to apply these to specific analytical problems in science.
We assume NO prior knowledge in computer coding: we will be teaching ‘from the ground up’. Matlab will be provided and is available to download free-of-charge. This module is not suitable for students who have already taken other university modules in computer coding. Admission from non-Clinical and Biomedical Sciences-degree programmes is at the discretion of the module lead. This module is part of the Proficiency in Data Science.
Module aims - intentions of the module
Analysing large dataset is increasingly common within research in biomedical sciences and neuroscience and to do so, programming skills are essential. For example, modern recording techniques allow scientists to record the activity of hundreds of cells (e.g. neurons) at once, and the only way to fully understand and integrate these complex dataset is to write ad-hoc code to extract relevant information.
The overall aim of this module is to develop some of the fundamental skills required to analyse and model complex data sets using a straightforward programming language. You will learn basic practical coding skills in a package commonly used in Medical Sciences: Matlab.
To introduce some areas of research where computer coding-based analysis is required, we will focus on specific case studies in areas such as data analysis and research methods, electrophysiology, image analysis and processing. Assessment will take the form of an in class exam and a scientific report with a code appendix.
Graduate attributes: as part of this module, you will develop the key employability skills of computer programming, data analysis, teamwork, project management, and preparing and scientific communication.
The module aligns with sustainable development goals (SDGs) SDG 3 (Good Health and Wellbeing) ensuring knowledge into physiology and healthy lives along with SDG 5 (Gender Equality) and SDG 10 (Reduced Inequalities).
Intended Learning Outcomes (ILOs)
ILO: Module-specific skills
On successfully completing the module you will be able to...
- 1. Develop skills to write appropriate algorithms for the analysis of scientific data.
- 2. Write efficient Matlab code for performing simple file management tasks.
- 3. Write efficient Matlab code for the preliminary analysis of complex data sets.
- 4. Justify and implement in Matlab some of the approaches used to analyse electrophysiological data.
- 5. Demonstrate an understanding and implementation of key digital signal processing techniques as applied to electrophysiological data and/or cardiac AP.
- 6. Learn to write code to quantify and manipulate features of biomedical imaging data
- 7. Demonstrate an awareness of, and an ability to implement, publicly available Matlab toolboxes generated by the wider scientific community.
- 8. Learn how to analyse data using Matlab, and how to draw scientific conclusions based on the data analysis
- 9. Describe statistical test used in research and learn how to perform statistical test in Matlab
ILO: Discipline-specific skills
On successfully completing the module you will be able to...
- 10. Select and implement appropriate analytical processes for a given biomedical data set.
- 11. Accurately present data in a graphical format.
ILO: Personal and key skills
On successfully completing the module you will be able to...
- 12. Evaluate analytical problems and design algorithm-based solutions.
- 13. Effectively use help functions, internet resources, manuals and books to solve problems.
- 14. Write clear data-driven reports on analysed data, including annotated code.
Syllabus plan
Whilst the module’s precise content may vary from year to year, an example of an overall structure is as follows:
We will begin the module with an introduction to fundamental coding skills using Matlab. Later in the module we will introduce some biomedical data analysis and modelling problems which can be addressed using these programming skills. Each of these areas are explored to introduce different analytical and coding skills.
- Introduction to coding in general and Matlab topics may include: Variable types; arrays and matrices; arithmetic in Matlab; indexing; built-in functions; plotting data; algorithms and pseudo-code; scripts and functions; annotating code with comments
- Data analysis in Matlab topics may include: statistical methods, structs and tables in Matlab, variables correlation, t-test and ANOVA
- Electrophysiology (brain and heart) topics may include: sampling theory, measuring peaks; batch processing; filtering.
- Image analysis topics may include: images as matrices; sampling theory; identifying regions of interest; measuring; averaging and filtering; images over time – movie.
- Cardiac action potential
Accessibility statement:
As part of this module, you will undertake sessions in the computing laboratory (of up to 80 students) that are typically 2 hrs in duration. Breaks are possible and students are welcome to leave the laboratory for short periods. In the event of unavoidable absence, it is possible to complete the computer practical tasks remotely.
Learning activities and teaching methods (given in hours of study time)
| Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
|---|---|---|
| 43 | 107 | 0 |
Details of learning activities and teaching methods
| Category | Hours of study time | Description |
|---|---|---|
| Scheduled Learning and Teaching | 5 | Lectures introductions and wraps 5x1 h |
| Scheduled Learning and Teaching | 30 | Computer labs 15x2h |
| Scheduled Learning and Teaching | 8 | Small groups activities |
| Guided Independent Study | 4 | Background information video 4 x 1 h |
| Guided Independent Study | 13 | Computer lab preparation and consolidation for the assessment |
| Guided Independent Study | 60 | Coding projects, including report writing throughout the module |
| Guided Independent Study | 15 | Revision |
| Guided Independent Study | 15 | Wider reading |
Formative assessment
| Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|
| Data analysis coursework 1 | 1000-word equivalent | 1-3,8-9,10-14 | Annotated code and verbal feedback |
Summative assessment (% of credit)
| Coursework | Written exams | Practical exams |
|---|---|---|
| 60 | 0 | 40 |
Details of summative assessment
| Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|---|
| Data analysis coursework 2 | 60 | 1000 word equivalent + code | 1-7,8-9,10-14 | Written |
| Practical coding exam | 40 | 2 hours | 1-13 |
Details of re-assessment (where required by referral or deferral)
| Original form of assessment | Form of re-assessment | ILOs re-assessed | Timescale for re-assessment |
|---|---|---|---|
| Data analysis coursework 2 | 60 | 1000 word equivalent + code | 1-7,8-9,10-14 |
| Practical coding exam | 40 | 2 hours + 15 minutes upload | 1-13 |
Re-assessment notes
If a student is referred in Coursework 2, they will be required to undertake a new equivalent assessment in the Ref/Def period.
Deferral – if you miss an assessment for certificated reasons that are approved by the Mitigation Committee, you will normally be either deferred in the assessment or an extension may be granted. If deferred, the format and timing of the re-assessment for each of the summative assessments is detailed in the table above ('Details of re-assessment'). The mark given for a deferred assessment will not be capped and will be treated as it would be if it were your first attempt at the assessment.
Referral - if you have failed the module (i.e. a final overall module mark of less than 40%) and the module cannot be condoned, you will be required to complete a re-assessment for each of the failed components on the module. The format and timing of the re-assessment for each of the summative assessments is detailed in the table above ('Details of re-assessment'). If you pass the module following re-assessment, your module mark will be capped at 40%.
Indicative learning resources - Basic reading
- Matlab:?a practical introduction to programming?and?problem solving?(2013) 3rd Ed. Stormy Attaway ISBN: 9780124058767 (available as an e-book from library)
- MATLAB for neuroscientists : an introduction to scientific computing in MATLAB (2014) 2nd Ed. Wallisch et al. ISBN: 9780123838360 (available as an e-book from library)
- Fundamentals of Digital Image Processing: a practical approach with examples in Matlab (2011). Chris Solomon, Toby Brecon. (available as e-Book)
Indicative learning resources - Web based and electronic resources
Module ELE page containing formative quizzes and access to on-line learning resources
Indicative learning resources - Other resources
- Matlab Style Guidelines 2.0: https://uk.mathworks.com/matlabcentral/fileexchange/46056-matlab-style-guidelines-2-0
| Credit value | 15 |
|---|---|
| Module ECTS | 7.5 |
| Module pre-requisites | None |
| Module co-requisites | None |
| NQF level (module) | 5 |
| Available as distance learning? | No |
| Origin date | 13/05/2024 |
| Last revision date | 04/02/2025 |
