Medical Data Science with Python or R

Course Description

This course offers a comprehensive introduction to the principles and practices of medical data science, with a focus on applying Python or R for the analysis of clinical and biomedical data. Emphasising both theoretical foundations and practical implementation, the course covers essential topics including data handling, statistical analysis, visualisation, machine learning, and reproducible research practices. Learners engage with real-world datasets such as clinical trials, electronic health records, and disease registries, developing proficiency in data cleaning, transformation, exploratory analysis, and model development. Ethical considerations, data governance, and regulatory compliance are integrated throughout to promote responsible data use. By the end of the course, participants will possess the skills to conduct rigorous, transparent, and clinically relevant analyses using open-source tools widely adopted in the biomedical research community.

Course Objectives

Upon completion, learners will be able to:

1. Demonstrate basic proficiency in programming using Python or R for data science applications.
2. Apply data cleaning and transformation techniques on structured medical datasets.
3. Perform exploratory data analysis and basic visualisation to interpret healthcare trends.
4. Conduct statistical analyses relevant to epidemiology and clinical research.
5. Integrate reproducibility and ethical considerations into medical data handling.

Course Outcome

By the end of the course, learners will be able to:

• Recognise the scope and importance of data science in healthcare.
• Demonstrate coding proficiency in Python or R for data handling.
• Apply appropriate data cleaning and transformation techniques.
• Analyse medical datasets using statistical and visual methods.
• Construct reproducible, ethically compliant research workflows.