Reproducible Health Data Science

Essential pre-course computer setup (1-2 hours):

Prior to the start of the course, participants will be given video instructions on how to setup their computers to be able to work through the course materials. It is essential that these steps are performed prior to the start of the course. There will be a pre-course online drop-in session to help any participants encountering problems preventing them from completing these steps. 

Main course: 

The 4 days of the online course will consist of a variety of learning activities set by tutors. Concepts, skills and tools will be demonstrated by a mix of live and pre-recorded videos and detailed online instructions. Participants will put theory into practice by setting up tools on their own computers, connecting to and using remote servers, and developing a realistic project over the duration of the course.  

Participants will be given a simple but real-world project comprising a quantitative health-informatics dataset and some ‘baseline’ analytical code written in R and Python. Participants are not expected to have specific scientific experience of this example data and analysis. Over the duration of the course, participants will learn new reproducibility concepts, skills and tools for tracking script versions, managing software dependencies, generating dynamic documents, constructing and maintaining pipelines, compiling packages, and more. Participants will apply these to the baseline project with guidance to make it more reproducible. By the end of the course, they will have a final working version that can be re-used as a template for their real-world projects.

All teaching will be conducted online using Blackboard and Microsoft Teams.  

Daily course timings:

29 January 2026 9:30 am to 4 pm
30 January 2026 10 am to 4 pm
2 February 2026 10 am to 4 pm
3 February 2026 10 am to 4 pm

By the end of the course participants should be able to: 

1. discuss the importance of reproducibility in health data science, including in the context of emerging AI coding tools;  
2. setup computers to securely and conveniently interact with different programming languages, filesystems and remote servers; 
3. understand principles of good project organisation using simple but scalable structures; 
4. integrate version control and pipelining systems into daily practice; 
5. apply techniques to make analytical code more readable, reliable and shareable; 
6. understand how to integrate code review into scientific practice; 
7. integrate dynamic documents into projects to create automated, sharable and expressive research outputs; 
8. understand different strategies towards reproducible software environments;  
9. describe approaches to reproducibility within trusted research environments; 
10. gain awareness of more advanced methods such as pipelining, containerisation and package development; and
11. handle and share data with minimum risk of loss or security violations. 

The course is intended for those who already have experience analysing health data and would like to learn how to improve the reproducibility of their work. It is an intermediate course. It does not include statistical instruction. We will not be using AI tools in this course though we will discuss how the principles that are important to reproducible research are in fact also critical to effective AI tool usage. It does not cover practices specific to qualitative data analysis. For the course project, we’ll be using observational quantitative cohort data from The Cancer Genome Atlas, though participants will not be required to have any specific knowledge about these data or its underlying scientific aspects. 

If your work involves a) obtaining or creating quantitative health data, b) performing complex analyses on those data independently or in collaboration with others, and c) creating scientific outputs to present those analyses, then this course is for you.  

This course will cover: 

1. the importance of and pitfalls preventing reproducibility;
2. how concepts of reproducibility borrow from software engineering, and how those same concepts are critical for AI based software development; 
3. setting up your compute environment, modern coding environments (VS Code) and relevant plugins;
4. transparent, portable and scalable organisation of project files and data (including project planning, data hygiene and security, directory management, modularity, portability);
5. integrating version control into daily practice using git and GitHub;
6. techniques to improve readability and reliability using code review, code linting, unit testing;
7. managing analytical software reproducibly;
8. using Quarto and Jupyter to create dynamic and shareable analysis reports;
9. reproducibility within trusted research environments;
10. (advanced) creating R packages that adhere to standards to share analytical functions and processes;
11. (advanced) pipelining tools to formalise the documentation and running of more complex pipelines (Snakemake); and
12. (advanced) containerization (Apptainer, Docker) and other approaches to software management (conda, mamba, renv).

All tutors are active researchers leading projects that involve the analysis of large health datasets in the MRC Integrative Epidemiology Unit (IEU) at the University of Bristol. Several have academic backgrounds in data science or related fields.  

To make sure the course is suitable for you and you will benefit from attending, please ensure you meet the following prerequisites before booking:

Before booking this course, please make sure you read the information provided above about the target audience and prerequisites. It is important that you have access to the relevant IT resources needed for the course and meet the knowledge prerequisites to ensure you can get the most from the course.

Bookings are taken via our online booking system, for which you must register an account. To check if you are eligible for free or discounted courses please see our fees and voucher packs page. All bookings are subject to our terms and conditions, which can be read in full here.

For help and support with booking a course refer to our booking information page, FAQs or feel free to contact us directly. For available payment options please see: How to pay your short course fees.

Participants are granted access to our virtual learning platform (Blackboard Ultra) 1 to 2 weeks in advance of the course. This allows time for any pre-course work to be completed and to familiarise with the platform.

To gain the most from the course, we recommend that you attend in full and participate in all interactive components. We endeavour to record all live lecture sessions and upload these to the online learning environment within 24 hours. This allows course participants to review these sessions at leisure and revisit them multiple times. Please note that we do not record breakout sessions.

All course participants retain access to the online learning materials and recordings for 5 months after the course. 

University of Bristol staff and postgraduate students who do not wish to attend the full course may instead register for access to the 'Materials and Recordings' version of this course: Further information and bookings.

100% of attendees recommend this course*.
*Attendee feedback from January 2026.

Here is a sample of feedback from the last run of the course:

“This is an interesting course and highly relevant to the current research environment." - Course feedback, January 2026

“Enjoyed the practical element - and the way in which we all had to work on these projects ourselves. Very knowledgeable and engaging organisers/moderators." - Course feedback, January 2026

“I didn't know about VS code and quarto. I'll definitely use these tools in the future. I also liked it that we learnt how to use jupyter notebooks. I thought they were very difficult to use but actually, they're not! Sometimes we're just too afraid to learn new things on our own. The course made it less daunting to try new software and code." - Course feedback, January 2026

“I found the content of the course very interesting and it matched to the description of the course. I felt there was a good balance between practicals and teaching sessions." - Course feedback, January 2026

“I thought each concept was introduced and shown very well. The use of a server similar to real life work places was also very appreciated and helpful. I learnt a lot and would recommend to anybody." - Course feedback, January 2026

“The practicals were well organised. I enjoyed that you could work through the exercises on your own, and then ask questions / discuss issues in breakout rooms when needed." - Course feedback, January 2026

“The pre-recorded walk-throughs for the practicals were very useful (being able to pause to follow at your own pace), and it's great that most of them were narrated - easier to follow what's going on. The memes were great, and so was the academic content and explanations of the process of performing different tasks and always proving rationale for specific good practices." - Course feedback, January 2026

“This course covered a huge range of topics in a short time. The lecturers were hugely knowledgeable and supportive during the practicals. The practicals were a great opportunity an well integrated across the course." - Course feedback, January 2026