Coding in Python for Health and Life Sciences
| Module title | Coding in Python for Health and Life Sciences |
|---|---|
| Module code | HPDM171 |
| Academic year | 2024/5 |
| Credits | 15 |
| Module staff | Dr Gareth Hawkes (Convenor) |
| Duration: Term | 1 | 2 | 3 |
|---|---|---|---|
| Duration: Weeks | 12 | 0 | 0 |
| Number students taking module (anticipated) | 36 |
|---|
Module description
Modern health research is becoming increasingly focused on the analysis of large, complex datasets. To extract meaningful information from such datasets, health data scientists often use computer programming languages to create bespoke analysis pipelines. Python is the most popular programming language for this task, making it a widely transferrable and employable skill.
This module assumes no prior knowledge of Python or any other computer coding language. We will be teaching Python from the ground up, starting with basic structures and objects available within Python, then developing more complex routines. When the fundamentals are established, you will learn how to manage and visualise data in Python. At the end of the course, you will learn how to perform machine learning tasks in Python, and come out of the module with general transferable computing and code-writing skills that will help you learn new languages quicker.
Module aims - intentions of the module
The overall aim of this module is to introduce students from a non-computing background to computer programming in Python, a common language for health data science. You will learn practical coding skills focused on developing the necessary skills to analyse data.
Intended Learning Outcomes (ILOs)
ILO: Module-specific skills
On successfully completing the module you will be able to...
- 1. Systematically write efficient and effective Python code for analysis of complex datasets
- 2. Use and critically evaluate common Python packages for processing, visualising data and performing machine learning
ILO: Discipline-specific skills
On successfully completing the module you will be able to...
- 3. Develop and implement efficient coding pipelines to clean and manage datasets
- 4. Visualise data and apply machine learning to address health data questions
ILO: Personal and key skills
On successfully completing the module you will be able to...
- 5. Write clear, data-driven reports on analysed data, including annotated code
- 6. Evaluate analytical problems and design algorithm-based solutions
Syllabus plan
Whilst the module’s precise content may vary from year to year, an example of an overall structure is as follows:
- An introduction to the Python environment and Notebook software
- The basics of writing efficient Python code and best practices
- Data structures available in Python
- Control structures such as functions, loops and conditions
- Data management, processing and cleaning with NumPy and Pandas
- Visualising data with seaborn and matplotlib
- Data mining and machine learning with scikit-learn
Learning activities and teaching methods (given in hours of study time)
| Scheduled Learning and Teaching Activities | Guided independent study | Placement / study abroad |
|---|---|---|
| 36 | 114 | 0 |
Details of learning activities and teaching methods
| Category | Hours of study time | Description |
|---|---|---|
| Scheduled learning and teaching | 12 | Lectures (12 X 1 hour lectures) |
| Scheduled learning and teaching | 24 | Workshops / tutorials (12 x 2 hours) |
| Guided independent study | 5 | Pre-recorded lectures on reproducible workflow (5 X 1 hour lectures) |
| Guided independent study | 109 | Background reading and preparation for module assessments |
Formative assessment
| Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|
| Online ELE quiz | Short Answer Questions | 1-6 | Written |
Summative assessment (% of credit)
| Coursework | Written exams | Practical exams |
|---|---|---|
| 100 | 0 | 0 |
Details of summative assessment
| Form of assessment | % of credit | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|---|
| Coding assignment | 100 | 2000 words | 1-6 | Written |
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 |
|---|---|---|---|
| Coding assignment | 2000 words (100%) | 1-6 | Typically within six weeks of the result |
Re-assessment notes
Please refer to the TQA section Referral/Deferral: http://as.exeter.ac.uk/academic-policy-standards/tqa-manual/aph/consequenceoffailure/
| Credit value | 15 |
|---|---|
| Module ECTS | 7.5 |
| Module pre-requisites | None |
| Module co-requisites | None |
| NQF level (module) | 7 |
| Available as distance learning? | No |
| Origin date | 30/10/2023 |
| Last revision date | 11/07/2024 |
