Design, implementation, and reflections on delivering machine learning workshops for medical microbiology and infection science

Increasingly, machine learning (ML) and artificial intelligence (AI) methods are being applied in medical microbiology and infection science. With this comes the challenge of educating professionals in these domains on the fundamental theory and methods of ML and AI in a way that is time-efficient and grounded in practical application. This article outlines the design, implementation, and reflections on delivering “Using Data Science and Machine Learning for Infection Science: A Hands-On Introduction”, a recurring one-day workshop created to introduce data science and ML concepts to infection science and medical microbiology students and professionals. The workshop provides participants with the opportunity for experiential learning using Orange, a no-code, open-source, and free-to-use data mining software application, where participants are exposed to the fundamentals of the ML lifecycle. By the conclusion of the one-day workshop, participants gain experience developing end-to-end analytical pipelines for tabular datasets, with a specific example focused on blood culture outcome prediction. The workshop emphasises transparency, reproducibility, and open science, promoting critical awareness of how data science can be applied within participants’ own domain specialty. This article details the curriculum design, practical implementation, and delivery of the workshop. Additionally, we reflect on the lessons learned and directions for future iterations of the workshop