Oxford Biomedical Data Science Training Programme

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. To date the programme has trained over 400 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. The programme delivers face-to-face lectures, tutorials and group exercises. Our multi-day 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.

To receive the latest information about courses and application dates please subscribe to our mailing list by emailing obds-announce-subscribe@maillist.ox.ac.uk

SUMMER 2026 Datacamp

Machine Learning in Functional Genomics (1 place left)

Where: Jesus College, University of Oxford

When: 21^st – 25^th September 2026 (9.30am -4.30pm)

This hands-on course will introduce participants to machine learning and deep learning approaches for genomic data analysis, covering topics including neural networks, model optimisation, transformers, and practical applications in functional genomics. The course is aimed at researchers with experience in Python and common data science packages (e.g. Numpy, Pandas and Matplotlib).

Open to all. Registration fee £500. Lunch and refreshments are included.

Accommodation available on site at Jesus college (£120 per night)

Register by 5pm on Friday 17^th July

Course Outline

Introduction to machine learning concepts and terminology
The machine learning workflow in Scikit-learn
Introduction to neural networks and deep learning – the perceptron
Building and training neural networks with PyTorch and GPUs
Classification of DNA features using convolutional neural networks
Regression of DNA binding signal
Model optimisation – hyperparameter tuning
Model interpretation – what has my model learned?
State of the art models – Transformers
Hackathon day – team competition to build the best performing model

Autumn 2026 COURSES

Late applications for non-bursary places are now open. Apply here:

https://app.onlinesurveys.jisc.ac.uk/s/oxford/obds-autumn-2026-late-application-form

Genomics on the Linux command line (one place left)

Four days over one week - 28/09/2026-01/10/2026, 9.30am-4pm Monday-Thursday
University of Oxford staff and postgraduate students: £800
External participants £960
Navigating the Linux command line
Editing and processing files in Linux
Configuring the Linux terminal and SSH
Software management with Conda
High Performance Computing with Slurm
Processing genomic data (RNAseq) on the command line

R for data science and genomics (one place left)

Prerequisites: basic knowledge of Linux command line (ls, cd, cp, ln, rm)
Nine days over three weeks (06/10/2026-08/10/2026, 12/10/2026-14/10/2026, 19/10/2026-21/10/2026) 9.30am-4pm (Tuesday-Thursday week 1, Monday-Wednesday weeks 2-3)
University of Oxford staff and postgraduate students: £1800
External participants £2160
Programming in base R
Data science in R with Tidyverse and ggplot2
Introduction to Bioconductor
RNAseq differential expression using DESeq2
Pathways: over-representation and gene set enrichment analysis

Introduction to single-cell RNAseq data analysis using R (two places left)

Prerequisites: familiarity with R programming, Tidyverse, ggplot2 and Bioconductor
Six days over two weeks (02/11/2026-04/11/2026, 09/11/2026-11/11/2026), 9.30am-4pm Monday-Wednesday
University of Oxford staff and postgraduate students: £1200
External participants £1440
Data processing with CellRanger
Single-cell RNAseq data analysis using Seurat
Introduction to data integration using Seurat
Single-cell RNAseq data analysis using Bioconductor
Investigating cell doublets and ambient RNA in Bioconductor
Pseudobulk differential expression analysis

Spatial Transcriptomics data analysis using R (two places left)

Prerequisites: Basic Linux commands, R scripting, familiarity with single cell RNAseq analysis principles (e.g. Seurat, Bioconductor)
Four days over 1 week (23/11/2026-26/11/2026), 9.30am-4.00pm Monday-Thursday
University of Oxford staff and postgraduate students: £800
External participants £960
No bursaries are available for this course, places will be assigned on a first come, first served basis.
This course covers end-to-end analysis of data from sequencing-based spatial transcriptomics platforms (e.g. Visium) in week 1 and in situ spatial transcriptomics platforms (e.g. Xenium) in week 2.
Participants will analyse real datasets to understand practical applications, advantages, limitations and common issues encountered in these techniques. The course will cover the following topics:

Overview of sequencing-based and imaging based spatial transcriptomics technologies
Common file formats and data structures
Raw sequencing data processing
Cell segmentation
Data quality control
Unsupervised clustering analysis
Data visualisation
Cell type deconvolution
Spatial statistics, niche and co-localisation analyses
Integrative and comparative analyses

Funded Places / Bursaries

The Cancer Research UK Oxford Centre offers two fully funded bursaries on every course except 'Spatial Transcriptomics data analysis using R'. 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).

The Oxford Biomedical Research Centre (BRC) offers one fully funded bursary on each course, except 'Spatial Transcriptomics data analysis using R'. To be considered for BRC funded places, projects must fall within the remit of the NIHR, which funds research for patient benefit. Applications will only be considered from candidates working on human samples. Priority will be given to applicants in research groups affiliated with BRC Themes. Details of all BRC Themes are available on the Oxford BRC website.

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