Computing Skills and Python

^HPDM206Z

Build core computing and Python skills for health data science. This short course introduces programming, data analysis and cloud computing techniques used to work with real-world health datasets.

This course is suited to:

This short course is suited to healthcare, life sciences or public health professionals, researchers, and graduates who want to develop practical computing and Python skills for working with health data. It is ideal for those from a non-computing background looking to gain employer-valued skills in data analysis, programming and collaborative coding.

What will I learn?

Health data science requires a broad set of computing skills to access, manage and analyse complex datasets. This short course introduces core concepts in computing for health data science, with a strong practical focus on programming in Python. You will learn Python from the ground up, starting with fundamental programming concepts before progressing to data management, visualisation and introductory machine learning techniques.

The course also covers essential tools used in modern data science, including cloud computing, Linux, SQL, Git and GitHub, and the ethical use of generative AI. Teaching is informed by current academic research and real-world health data practices, ensuring the skills you develop are directly applicable to professional and research settings.

By the end of the course, you will have gained transferable computing and coding skills that provide a foundation for further study or career development in health data science.

Learning outcomes

• Demonstrate understanding and competence in fundamental skills for health data science.

• Highlight the differences between computational tools and how to combine them to analyse health data.

• Systematically write efficient and effective Python code for analysis of complex datasets.

• Use and critically evaluate common Python packages for processing, visualising data and performing machine learning.

• Version control for reproducible analysis pipelines.

• Using Openstack and the Linux command line to perform advanced computing tasks.  

• Develop and implement efficient coding pipelines to clean and manage datasets.

• Visualise data and apply machine learning to address health data questions.

• Working collaboratively on developing software.

• Dynamically learning new computing skills.

• Write clear, data-driven reports on analysed data, including annotated code.

• Evaluate analytical problems and design algorithm-based solutions. 

How is the module assessed?

For this course, you should expect to engage in structured learning activities for 10-15 hours per week on average, plus additional time spent on self-directed learning (such as further reading or preparing for assessments).

The taught course can be completed in 12 weeks, with the final submission in week 11. Marking and feedback are provided after this, in line with University policy.

Module staff

Entry Requirements

We will consider applicants with a 2:2 Honours degree with 53% or above, or who are coming from a different academic background (that is equivalent to degree level) but who also have relevant work experience.

English language requirements

International students need to show they have the required level of English language to study this course.

The required test scores for this course fall under Profile B2.