Oxford Biomedical Data Science Training Programme

The Oxford Biomedical Data Science Training Programme, funded by Wellcome and the Oxford Biomedical Research Centre, is designed to train biomedical scientists in the skills and methods required for the analysis and interpretation of large-scale biomedical datasets, particularly genomic and functional genomic data.

This unique programme runs three times per year, with timings aligned to the University of Oxford terms. Numbers are limited and application is through a competitive process. The application schedule for 2019 can be found below. Training takes the form of six weeks of group lectures, tutorials and exercises, followed up with weekly code clinics. The programme is open to all University of Oxford staff and D.Phil. students and costs £6000.

Three scholarships per cohort have been generously funded by the Precision Medicine Cluster of the Oxford NIHR Biomedical Research Centre. Places will be awarded by a review committee based on the scientific quality of the proposed project and training needs of the individual. To be considered for a BRC scholarship, projects must fall within the remit of the NIHR, which funds research for patient benefit, and must focus on analysis of human samples. Priority will be given to applicants in research groups affiliated with one of the five themes of the BRC Precision Medicine Cluster, namely Multi-modal Cancer Therapies, Molecular Diagnostics, Genomic Medicine, Respiratory, and Haematology and Stem Cells. However, applications from all BRC Themes will be considered if there are sufficient spaces available. Details of all BRC Themes are available on the Oxford BRC website.

To apply please fill in this form and email it to david.sims@imm.ox.ac.uk

Training Schedule

Subject	Topics
Unix	Managing your software environment (Conda) Navigating file systems, managing processes, manipulating text files, running genomics tools
Python	Basic programming concepts, code organisation, algorithm design, debugging.
Advanced Python	Version control, object-oriented programming, Python for computational genomics, Python for data science (pandas)
Genomics pipelines	High performance computing, automating workflows in Python, genomics pipelines: RNAseq, ChIP-seq, ATAC-seq, single-cell RNAseq
Interpretation of genomic data	Enrichment analysis, pathway analysis, network analysis
R & Statistics	R syntax and data structures, Tidyverse, differential gene expression, pathway analysis, PCA, clustering
Machine Learning	Linear regression, random forests, deep learning

Course Dates

Cohort	Applications Open	Applications close	Course starts	Course Ends
January 2019	Closed	Closed	14/01/2019	22/03/2019
May 2019	Closed	Closed	13/05/2019	05/07/2019
September 2019	15/05/2019	24/06/2019	16/09/2019	25/10/2019*