Description
This course aims to familiarise biomedical students and researchers with principles of Data Science. Focusing on utilising machine learning algorithms to handle biomedical data, it will cover: effects of experimental design, data readiness, pipeline implementations, machine learning in Python, and related statistics, as well as Gaussian Process models.
Providing practical experience in the implementation of machine learning methods relevant to biomedical applications, including Gaussian processes, we will illustrate best practices that should be adopted in order to enable reproducibility in any data science application
Prerequisites
- Students and researchers from life-sciences or biomedical backgrounds, who have, or will shortly have, the need to apply the techniques presented during the course to biomedical data.
- The course is intended for those who have basic familiarity with the Python scripting language.
- We recommend either attending (See "Related courses" below), or working through the materials of An Introduction to Solving Biological Problems with Python before attending this course.
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