Data-Driven Life Sciences course 2026
Description
Registration is open for the Data-Driven Life Sciences course 2026, starting 24 August 2026 at 10:00 (CEST). The course is open to Master's students, PhD students, postdocs, and researchers.
This fully online course explores the intersection of data science, AI, and life sciences through six modules combining lectures, hands-on computer labs, interactive journal clubs, and a final project. Participants work with real data from bioimaging, structural biology, transcriptomics, genomics, systems biology, and multi-omics, while learning to build and use AI-assisted research workflows.
Highlights for 2026
This year's computer labs include:
- Vibe Coding — coding with AI assistance
- AI Agents — building agentic workflows for coding, data analysis, and automation
- MCP Tools — creating modular AI-powered tools for different DDLS topics
Course details
- Schedule: View the full course schedule
- Start: 24 August 2026
- Teaching period: 24 August–2 October 2026, followed by the final project
- Full course period: through 23 October 2026
- Format: Online via Zoom; mostly pre-recorded lectures plus weekly live computer labs and journal clubs
- Credits: 7.5 ECTS
- Fee: Free of charge
For further information, visit the course website or contact ddls-course@scilifelab.se or wei.ouyang@scilifelab.se.
Content Providers
Learning Outcomes
By the end of the course, participants 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 and 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 across collection, handling, sharing, and analysis
- Reflect on limitations, biases, risks, and ethical considerations of AI
- Reflect on broader ethical implications of data-driven life sciences
Prerequisites & Technical Requirements
Prerequisites
Participants are recommended to:
- Become familiar with the SciLifeLab Data-Driven Life Science initiative and the concept of the data life cycle
- Refresh core Python basics, including variables, data types, control flow, functions, modules, simple plotting, and reading/writing files
Preparation resources are available on the course prerequisites page.
Technical requirements
For the online labs, participants need:
- A computer with reliable internet access
- A modern web browser
- A Google account for Google Colab and Drive storage
- A GitHub account for versioning and sharing notebooks/code
- Optional but encouraged: access to an AI coding or assistant tool; free tiers are sufficient
Topics & Tags
Affiliations & Networks
Activity log
