The Health Data Science CDT features two terms of intensive training on the foundations of computational statistics, machine learning and data engineering and the application of those tools to a spectrum of topics in health and biomedical research. Each morning’s lectures are followed with afternoon practical computational exercises that consolidate the lecture material. Each term has a capstone event, a two-week data challenge, where the entire cohort uses an at-scale data set to address a current health research question. The remainder of the first year is spent on two 8-week research placements, one of which usually leads to a dissertation project. Download the course overview for the first two terms below (subject to change).
During Trinity Term and the summer each student will undertake two 8-week research placements in two of their chosen research areas. This provides students with experience of working as a member of an existing research group within the Big Data Institute or any faculty member associated with the CDT. All projects are supervised by an Oxford academic, but some are in collaboration with industrial or other partner organisations. It is expected that one of the projects will become the basis of the student’s dissertation topic, starting from Michaelmas Term of year two.
A dissertation with continued transferable skills and ethics training as well as cohort workshops. Training with ORBIT.