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

The course comprises six required weekly modules plus a final project and awards 7.5 ECTS upon completion.

Most lectures are pre-recorded and released at the start of each module week. Two lecture sessions are live: the Module 1 introduction and the Module 6 final-project briefing. Each week also includes a live computer lab on Wednesday, 13:00–17:00, and a live journal club on Friday, 10:00–12:00. All live sessions are held online via Zoom.