Quantitative approaches are now ubiquitous used throughout biomedical research and it has become increasingly important for biomedical researchers to include training in advanced statistics, computation and data science in order to maintain modern research programs.

This course is designed to provide biochemistry graduate students with the opportunity to efficiently learn quantitative tools and techniques relevant to their research regardless of their prior training background. Topics include programming, software development, permutation and randomization testing, stochastic modelling, machine learning, artificial intelligence, generative modelling, data cleaning, and visualization.

The course does not expect students have an particular background. The syllabus/structure of the course is such that a student can declare what they way to learn in this domain and we will set up a timeline and method for evaluation for you.

So if you want to learn programming in Python or R, if you want to learn statistics or modelling, or you want to learn the basics of deep learning or AI, then we can set this up to complement your research project.