📊 HT 2026: Data-Driven Life Sciences

Welcome to the 2026 Data-Driven Life Sciences (DDLS) course — a hands-on master’s course in AI, biological data, and scientific computing. Across six modules you will learn to use AI to solve biological problems: working with real data (genomics, imaging, proteins, multi-omics), building and using AI agents, and designing AI-assisted research workflows.

Guest lecturers (DDLS Fellows, SciLifeLab Fellows, and SciLifeLab facility trainers) present real data, methods, and models underpinning current biological research. The course is primarily aimed at KTH Master’s students, and is also open to PhD students and other interested participants.

Registration

Registration for DDLS 2026 is open. The course is offered online and free of charge.

• Sign up for DDLS 2026

How to register depends on your affiliation:

• KTH Master’s students: register in Ladok, course code SK2538.
• KTH PhD students: register in Ladok, course code FSK3538, or email the PhD program administrator.
• Master’s students from other Swedish universities: contact your program administration for approval before signing up.
• PhD students from other universities and other participants: sign up via the form above; we can issue a certificate of participation upon completion (check with your program administrator that the course can be credited).

If you do not need course credits, you are welcome to join the full course or individual modules; a certificate can still be provided.

Intended Learning Outcomes

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

Course Format and Credits

The course consists of six required modules. Each module spans one week with three fixed sessions: a lecture on Tuesday 10:00–12:00, a hands-on computer lab on Wednesday 13:00–17:00 (Google Colab notebooks), and a journal club on Friday 10:00–12:00. Completing all modules plus the final project yields 7.5 ECTS.

Credit breakdown:

• Computer labs — 2.0 hp
• Final project — 3.0 hp
• Oral exam (presentation) — 2.5 hp

Modules and Certification

Both the computer lab and the journal club are mandatory and pass/fail assessed.

• Labs: You receive a Jupyter notebook with guided exercises. Work in Google Colab, discuss during the session, and submit your completed notebook for evaluation.
• Journal club: You address a standard question set each week while discussing the assigned paper. Participation and contributions are graded.

Final project: You carry out your own project on a topic that relates to the content of the course, culminating in a project report (all participants). Master’s students additionally give a mandatory oral presentation, graded A–F, which is the course grade. PhD students and other participants are assessed pass/fail, and their oral presentation is optional. Details will be announced during the course.

To receive ECTS credits or a certificate of participation you must actively attend and engage in all required sessions.

Self-Directed Learning

A core objective is to strengthen your ability to “learn how to learn.” You are encouraged to use modern AI tools (e.g., ChatGPT, Claude, Gemini) to explore concepts, draft code, and critique analyses—while remaining accountable for verifying outputs, documenting usage, and recognizing limitations and biases. This approach is integral to both labs and journal clubs.

Course Modules

Note: The topics and guest lecturers for the six modules are being finalized and will be announced ahead of the course start. See the schedule for dates and session times as they are confirmed.

• Module 1 — Topic and lecturer to be announced
• Module 2 — Topic and lecturer to be announced
• Module 3 — Topic and lecturer to be announced
• Module 4 — Topic and lecturer to be announced
• Module 5 — Topic and lecturer to be announced
• Module 6 — Topic and lecturer to be announced
• Final Project — Apply what you learned to your own project (report for all; presentation for Master’s)

Assessment

Computer labs and journal clubs are pass/fail for everyone (attendance + satisfactory notebook / engaged participation).

Master’s students:

• Final project report — pass/fail
• Oral presentation (oral exam) — mandatory, graded A–F (this is the course grade)

PhD students and other participants:

• Final project report — pass/fail
• Oral presentation — optional
• Overall assessment: pass/fail

Peer review precedes final grading and feedback by the teaching team.

Communication and Groups

Announcements and posts: here
Questions: use the contact page here or email ddls-course@scilifelab.se.

Instructors

You will meet:

• Wei Ouyang, Assistant Professor in Biophysics (lectures, journal clubs, grading) wei.ouyang@scilifelab.se (course responsible)
• Songtao Cheng, PhD student (lectures support, labs, journal clubs) songtao.cheng@scilifelab.se
• Nils Mechtel, PhD student (lectures support, labs, journal clubs) nils.mechtel@scilifelab.se

Schedule

See the course schedule for dates and session details.

FAQs