ELIXIR node event Data-driven Life Sciences course 2025

View event

Date: 26 August - 16 October 2025

Language of instruction: English

Application Deadline: No date given

We are excited to announce the Data-driven Life Sciences course 2025, starting Tuesday, August 26 at 13:00 (CET). A few spots remain available for Master's, PhD students, postdocs, and researchers -- apply now to secure your place!

This fully online course explores the intersection of data science, AI, and life sciences, combining lectures, hands-on computer labs, and interactive journal clubs.

Highlights for 2025

In addition to core DDLS topics (bimaging, structural biology, transcriptomics, system biology, etc.), this year's computer labs will focus on:

  • Vibe Coding -- coding with AI assistance

  • AI Agents -- building agentic workflows for coding, data analysis, and automation

  • MCP Tools -- creating your own modular AI-powered tools for different ddls topics

Course Details

  • Schedule: 

  • Start date: August 26, 2025

  • End date: October 16, 2025

  • Format: Online via Zoom (1 module per week, 3 sessions per module)

  • Credits: 7.5 ECTS (whole course)

  • Audience: Master's students, PhD students, postdocs, and researchers

  • Fee: Free of charge

👉 Apply here: Registration Form

For further information, please visit the  or contact ddls-course@scilifelab.se or wei.ouyang@scilifelab.se.

We look forward to welcoming you to an inspiring course and to exploring how AI and data-driven methods are reshaping life science research.

Contact: ddls-course@scilifelab.se

Keywords: Data science, AI, Life sciences

Country: Sweden

Prerequisites:

Be prepared

As prerequisites for the course, we recommend becoming familiar with the following:

  • Browse the SciLifeLab Data-Driven Life Science (DDLS) initiative to understand national priorities and the concept of the data life cycle, which is central in this course.
  • Refresh core Python basics (variables, data types, control flow, functions, modules, simple plotting, reading/writing files). See the resources below.

Technical setup for labs (all online):

  • A computer with reliable internet access
  • A modern browser (e.g. Chrome)
  • A Google account (for Google Colab and Drive storage)
  • (Optional but encouraged) Accounts for AI coding/assistant tools (e.g. ChatGPT); free tiers are sufficient
  • A GitHub account for versioning and sharing notebooks/code

Learning objectives:

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

  • Describe the field of data-driven life sciences
  • Summarize major application areas and their data types
  • Give examples of typical analysis workflows
  • Apply core statistical and machine learning methods to biological datasets
  • Formulate simple models of biological phenomena
  • Employ AI tools/agents to support reasoning, problem solving, and exploration
  • Critically evaluate and responsibly integrate AI outputs into analyses
  • Collaborate effectively with AI-assisted tools to enhance research productivity
  • Present and review scientific literature
  • Practice sound data management (collection, handling, sharing, analysis)
  • Reflect on limitations, biases, risks, and ethical considerations of AI
  • Reflect on broader ethical implications of data-driven life sciences

Target audience: Masters students, PhD Students, postdocs, researchers

Cost basis: Free to all

Activity log