Data Science Tools and Workflows for Health Data Research

About This Course

This course provides an accessible entry point into the transformative role of data science in healthcare. As healthcare systems generate exponentially growing volumes of data from electronic health records, wearables, and diagnostics, there is a critical skills gap in effectively analysing this information. This pathway bridges that gap by introducing the fundamental tools, workflows, and ethical considerations necessary to uncover patterns, predict outcomes, and generate actionable insights. Learners will explore real-world applications, from personalised medicine to resource optimisation, utilising open-source tools like R and Python, alongside Large Language Models (LLMs). By understanding the data science lifecycle, from problem definition to model deployment, participants will be prepared to engage with advanced Health Data Research (HDR) initiatives and apply data-driven solutions to improve patient care and operational efficiency.

Learning Objectives

By completing this training, learners will be able to:

• Explain the role of data science in healthcare, identifying the types of data used and the key steps in a typical data science workflow.

• Describe common data science tools (R, Python) and machine learning techniques (supervised, unsupervised, neural networks) used to solve healthcare challenges.

• Utilise Large Language Models (LLMs) responsibly as a learning aid for code generation, debugging, and concept explanation.

• Recognise and evaluate various forms of bias (historical, sampling, confounding) in healthcare data to ensure fair and accurate conclusions.

• Demonstrate basic data literacy by communicating data science concepts and model evaluation metrics to varied audiences.

Intended Audience

This course is designed for individuals who are relatively new to data science but possess some academic background in a STEM subject. It is particularly suitable for students, interns, clinical staff, or healthcare professionals who are new to working with healthcare data. No prior programming or advanced statistical experience is required, only a curiosity to learn and a desire to understand how data science can be applied to real-world healthcare challenges to improve patient outcomes and system efficiency.