Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

The Oxford Biomedical Data Science Training Programme delivers training in the skills and methods required for the analysis and interpretation of large-scale biomedical datasets, particularly genomic and functional genomic data.

The OBDS programme was established in 2018 with funding from Wellcome, the Cancer Research UK Oxford Centre and the NIHR Oxford Biomedical Research Centre, following a successful pilot programme within the MRC Computational Genomics Analysis and Training programme. To date the programme has trained over 50 biomedical researchers, who have benefited from the course in different ways (see testimonials).

This unique programme runs three times per year, with timings aligned to the University of Oxford terms. Numbers are limited to 8 participants per cohort and application is through a competitive process. The programme delivers  face-to-face online lectures, tutorials and group exercises. Our multi-week format and small group size enable us to go step-by-step through real world scenarios from setting up software environments to processing data and visualisation of results. The interactive format encourages participants to lead group exercises; quickly building the confidence and resilience required for independent work. After the course, researchers are supported by regular weekly code clinics and one-day workshops in specialised topics. 

The OBDS programme is currently 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

The Cancer Research UK Oxford Centre offers two fully funded places per term. To be considered for CRUK funding,  research projects must be cancer research focused and applicants must be CRUK Oxford Centre members (you can sign up here). CRUK funding will be allocated by representatives of the Centre Management Group on the basis of both application quality and fit with Centre strategy (details of which can be found here).

 For more information please contact


Course content



Computer systems

  • Linux command line: navigating file systems, managing processes, manipulating text files, running bioinformatics tools
  • Managing your software environment using Conda
  • High Performance Computing using Sun Grid Engine / Slurm
  • Version control with Git and GitHub


  • Basic programming concepts
  • Code organisation
  • Algorithm design
  • Debugging
  • Object-oriented programming
  • Python for computational genomics

Python Data Science

  • Data manipulation (Numpy, Pandas)
  • Data Visualisation (Matplotlib, Seaborn)
  • Introduction to machine learning
  • Linear regression
  • Dimensionality reduction & clustering
  • Classification (Random forests, SVMs)

Genomics pipelines in Python

  • Automating workflows in Python
  • RNAseq
  • ChIP-seq / ATAC-seq
  • Variant calling
  • single-cell RNAseq

R for Data Science

  • R syntax and data structures
  • The RStudio IDE
  • Tidyverse
  • Probability distributions and statistical tests
  • PCA and clustering

R / Bioconductor Packages for Genomics

  • Differential gene expression
  • Pathway analysis
  • Network analysis
  • single-cell RNAseq


Upcoming Courses

Cohort Applications Open Applications close Course starts Course Ends
January 2021 26/10/2020 closed


Please note this course will run over 7 weeks, 4 days per week, with a break over half term. 

May 2021 08/02/2021 01/03/2021


Please note this course will run over 7 weeks, 4 days per week, with a break over half term. 


* Course dates do not include follow-upcode clinics

Guest Speakers