This course will introduce life scientists to practical data science methods increasingly used in research, with a particular focus on statistics and machine learning, using Python as the language of choice. Participants will learn how to prepare, process, and visualise some key biological datatypes, and gain hands-on experience in training and evaluating machine learning models using publicly available data. 

The course will combine morning lectures (supported by short practical exercises where relevant) with afternoons working on group projects, providing independent, hands-on experience in applying what is learnt to research questions. Trainers and mentors will be available throughout to guide participants and offer advice and feedback.
 

Group projects

A central part of the course is the group project. You will work in small groups, applying machine learning approaches to a biological question of interest. These projects will follow a framework suggested by the mentors, but be driven by participants, with as much support and input from mentors as you need.

During the application process, you will be asked to indicate your preferences from a list of biological applications for the group project work. During the course, following a framework provided by project mentors, each group will finalise their project aims, collect and process data, and apply machine learning approaches to address their research question. Groups will receive regular feedback and support from mentors, and are expected to take an active role in driving their projects forward collaboratively.

There will be daily opportunities to present progress so far and receive feedback, and the projects will end with a flash-talk session on the last day, giving participants the opportunity to present the data, methods, and insights they have developed during the training.

Who is this course for?

This course is aimed at life scientists who are beginning to integrate data science approaches into their research, and who wish to develop practical skills in programming, data handling, and machine learning. It will be particularly suitable for PhD students and early-career researchers who are ready to start analysing their own data.

While no prior experience in machine learning is expected, a beginner level in Python is required. We recommend going through this free online training: https://swcarpentry.github.io/python-novice-gapminder/. Further materials may be sent to selected applicants before the course.

What will I learn?

Learning outcomes

By the end of the course, you will be able to:

• Use Python to collect, handle, and visualise biological data
• Identify analysis methods suitable for particular datasets
• Apply preprocessing pipelines and good practices for analysis and reproducibility
• Apply statistical methods to biological data
• Train and evaluate simple machine learning models
• Discuss applications of large language models in the life sciences

Course content

During this course you will learn about:

• APIs and Python for data collection 
• Data preprocessing and visualisation in Python
• Core machine learning methods and evaluation
• Deep learning approaches, including neural networks and transformers
• Large language models and their potential applications in the life sciences

Please read our support page before starting your application. To be considered for a place on this course, you must do the following:

• Complete the online application form.
• Ensure you add relevant information to the ‘submission details’ section where you are asked to provide information on your:
  • pre-requisite skills and knowledge
  • current work and course expectations
  • data availability
• Upload one letter of support from your supervisor or a senior colleague detailing reasons why you should be selected for the course.

Please submit all documents during the application process by 23:59 GMT on 01 March 2026. Items marked * in the application are mandatory. Incomplete applications will not be processed. 

All applicants will be informed of the status of their application (successful, waiting list, unsuccessful) by 16 March 2026. If you have any questions regarding the application process, please contact Juanita Riveros. 

Fees

 The registration fee includes:

• Catering as detailed on the course programme
• Accommodation for four nights (15, 16, 17, and 18 June)
• Bespoke course handbook with links to all course materials
• Use of a computer in the EMBL-EBI training suite throughout the course
• Secure virtual machines for the practical sessions listed in the programme
• Shuttle bus on the final course day to Cambridge train station 

* If your company is an EMBL-EBI Industry Programme Member, discounts may be available. This will apply automatically on registration if applicable; alternatively, please contact your Event Organiser for more information.

Financial assistance

Financial assistance is available to a limited number of participants attending this course.

Registration fee waivers

A limited number of registration fee waivers are offered for this course.

• Application process:
  • Apply for the fee waiver alongside your course application.
  • Provide a brief explanation of why you require the waiver and how attending the course will benefit your career.
• Notification:
  • You will be informed of your waiver status at the same time as the outcome of your course application.
  • If awarded, the registration fee will be waived entirely.

Travel grants

A limited number of travel grants of up to £1,000.00 are available to support participants' travel expenses.

• Covered expenses:
  • Airfare, train, bus, taxi, and visa costs.
• Application process:
  • Apply for the travel grant when submitting your course application.
  • You will be informed of the grant decision, including the amount awarded, along with your course application outcome.
• Reimbursement process:
  • Participants must pay upfront for their travel costs.
  • A reimbursement form will be provided upon course completion.
  • Submit the completed form with receipts within one month of travel completion.
• Grant adjustment:
  • The organisers may adjust the grant amount to accommodate more participants.

Financial assistance terms and conditions

• Selection process: Recipients of financial assistance will be selected by the scientific organisers during the course application review.
• Selection criteria:
  • Selection is based on scientific merit, your current work or study location, the need for financial support, and the career impact of attending the course.
  • Priority will be given to applicants from Low- and Middle-Income Countries (LMICs).
• Fair consideration: Applying for financial assistance will not impact the outcome of your course application.

Additional information

Travel information

Electronic Travel Authorisations (ETAs) are needed for visitors to the UK who do not currently need a visa for short stays, or who do not already have a UK immigration status. Full details and information on how to apply can be found at the UK Gov website. 

Event terms and conditions

• Selection process: Recipients will be selected by the scientific organisers during the course application review.
• Selection criteria:
  • Selection is based on scientific merit, your current work or study location, the need for financial support, and the career impact of attending the course.
  • Priority will be given to applicants from Low- and Middle-Income Countries (LMICs).
• Fair consideration: Applying for financial assistance will not affect your course application outcome.