Coding for Medical Scientists
| Module title | Coding for Medical Scientists |
|---|---|
| Module code | CSC2020 |
| Academic year | 2023/4 |
| Credits | 15 |
| Module staff | Dr Federico Palmisani (Convenor) Dr Jonathan Witton (Convenor) |
| Duration: Term | 1 | 2 | 3 |
|---|---|---|---|
| Duration: Weeks | 0 | 11 | 0 |
| Number students taking module (anticipated) | 45 |
|---|
Module description
Analysing large complex data sets is increasingly common within biomedical science specialisms such as neuroscience. To extract meaningful information from such data sets, scientists often use computer programming languages to create bespoke analysis routines. For example, modern recording techniques allow neuroscientists to record the activity of hundreds of neurons at once and the only way to fully understand and integrate these complex data sets is to write specifically tailored computer code to extract relevant information.
In this practical module, we will introduce you to Matlab, a programme commonly used across the medical sciences for carrying out analyses of complex data sets. Most of the contact time in this module will consist of computer lab workshops, where you will learn the fundamentals of writing Matlab code. Once you have grasped these underpinning skills, you will learn how to apply these to specific analytical problems in medical science and neuroscience research.
Importantly, we assume NO prior knowledge or skills in computer coding: we will be teaching these ‘from the ground up’. Matlab will be provided in the computer labs and is available to download free-of-charge for your personal computer. This is an optional module for BSc Neuroscience, Medical Sciences and Sport & Exercise Medical Sciences. It 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.
Module aims - intentions of the module
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 the following areas:
- Data analysis and methods in research
- Electrophysiology in neuroscience
- Image analysis and processing
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.
- 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.
Section 1: 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
Section 2: Data analysis in Matlab
Topics may include: statistical methods, structs and tables in Matlab, variables correlation, ttest and ANOVA
Section 3: Electrophysiology
Topics may include: sampling theory, measuring peaks; batch processing; filtering.
Section 4: Image analysis
Topics may include: images as matrices; sampling theory; identifying regions of interest; measuring; averaging and filtering; images over time – movies.
Learning activities and teaching methods (given in hours of study time)
| Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
|---|---|---|
| 40 | 110 | 0 |
Details of learning activities and teaching methods
| Category | Hours of study time | Description |
|---|---|---|
| Scheduled Learning & Teaching activities | 2 | Lectures introductions and wraps 2x1h |
| Scheduled Learning & Teaching activities | 30 | Computer labs 15x2h |
| Scheduled Learning & Teaching Activities | 8 | Small groups activities |
| Guided Independent Study | 4 | background information video 4 x 1h |
| Guided independent study | 16 | Computer lab preparation and consolidation |
| Guided independent study | 60 | Coding projects, including report writing |
| 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 + code | 1-3,8-9,10-14 | Written |
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 | 1500 word equivalent + code | 1-7,8-9,10-14 | Written |
| Practical coding exam (open book) | 40 | 2 hour + 30 min upload time | 1-13 | Written |
| 0 | ||||
| 0 | ||||
| 0 | ||||
| 0 | ||||
| 0 |
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%) | Data analysis coursework 3 (1500 word equivalent + code ) (60%) | 1-7,8-9,10-14 | Ref/Def period |
| Practical coding exam (open book) (40%) | Practical coding exam (open book) (2 hour + 30 min upload time) (40%) | 1-13 | Ref/Def period |
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.
Please refer to the TQA section on Referral/Deferral: http://as.exeter.ac.uk/academic-policy-standards/tqa-manual/aph/consequenceoffailure/
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
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 | 18/01/2019 |
| Last revision date | 24/02/2023 |
