Data-Driven
Life Sciences
A hands-on course where you master the AI tools, data science workflows, and biological reasoning skills that are reshaping how science is done — and how researchers stay competitive.
You're Graduating Into a Different World
Every decade, a shift in tools changes how knowledge workers operate. The current shift is the biggest yet — and it's happening right now.
How Biological Research Has Evolved
The scientific method itself is being transformed — from a purely human-driven process to one where AI plays an increasingly central role in every step.
Where Biology Meets Computation
Modern biology generates more data than any human can process. Data-driven life sciences is the discipline of using machine learning, AI, and computational methods to extract meaning from this data — and to make discoveries that would be impossible otherwise.
Each week brings a new domain, taught by leading researchers from SciLifeLab and KTH.
Learning, Augmented by AI
Every week combines expert knowledge with AI-assisted hands-on practice — the same workflow you'll use in your research career.
What Past Students Built
The final project is 3 weeks of building something real — AI-powered tools, web apps, and research demos used by actual labs.
Explore top courses
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.
Module 2 - Image Analysis and Microscopy
Start date: Week 36 (2nd September 2025) Lecturer: Estibaliz Gómez de Mariscal (Postdoc at Instituto de Tecnologia Química e Biológica António Xavier (ITQB NOVA), NOVA University of Lisbon, Portugal; Tuesday 2nd September 13:00-15:00 CEST) Estibaliz Gómez de Mariscal completed her PhD in Mathematical Engineering in 2021 and is currently an EMBO Postdoctoral Fellow in the Optical Cell Biology Group at Instituto de Tecnologia Química e Biológica António Xavier (ITQB NOVA), NOVA University of Lisbon, Portugal.
Course Schedule
Important Dates: Application closes: Aug 25, 2025, 24:00 CEST First lecture: Aug 26, 2025, 13:00 CEST Final Project plan submission deadline: Oct 6, 2025 Final Project: Oct 6-29, 2025 Final Project consultation 1: Oct 8, 2025, 13:00-14:00 Final Project consultation 2: Oct 20, 2025, 13:00-14:00 Final Project report submission deadline: Oct 29, 2025, 23:59 Oral Presentation (only for master’s students): Oct 31, 2025, 9:00-12:00 Lectures, computer labs and seminars will be held online via Zoom.
📊 HT 2025: Data-Driven Life Sciences
Course content for the 2025 Data-Driven Life Sciences (DDLS) course.
Final Project — Exploring and Modeling Life Science Data with AI Agents
⚠️ Note: This is a draft project plan. Details (including deadlines, deliverables, and evaluation criteria) may change. Updates will be announced on the course website and in class. Welcome to the final project of DDLS 2025.
Module 4 - Single-cell Transcriptomics and Genomics
Start date: Week 38 (16th September 2025) Lecturer: Xiaojie Qiu Lecture: Tuesday, 16 September 2025 — 08:00 CET (Pacific Time: Monday, 15 September, 11:00 PM) Module 4 covers genomics and single-cell analysis featuring scRNA-seq processing pipelines, pathway analysis, and genomic workflows.
Module 1 - Introduction to Data-Driven Life Sciences
Start date: Week 35 (26th August 2025) Lecturer: Wei Ouyang (DDLS Fellow; Tuesday 26th August 13:00-15:00 CEST) Module 1 serves as the foundational introduction to the Data-Driven Life Sciences (DDLS) course.
Module 6 - Automated Scientific Discovery and AI Agents
Start date: Week 40 (30th September 2025) Module 6 focuses on automated scientific discovery and AI agents. You will learn how to design, build, and evaluate agentic workflows that plan experiments, orchestrate tools and data pipelines, and interface with lab automation.
Module 3 - Protein Structure and Molecular Biology
Start date: Week 37 (9th September 2025) Speaker 1: Patrick Bryant (Ass. Prof. at SU & DDLS Fellow) Patrick Bryant’s research seeks to answer questions about the evolution of proteins and how this information can be used to create a new range of AI tools.
Module 5 - Precision Medicine and Systems Biology
Start date: Week 39 (23rd September 2025) Module 5 focuses on clinical applications of AI and machine learning with an emphasis on multi-omics integration, biomarker discovery, and clinical data analysis. The module highlights how biologically informed models and novel machine learning methods can advance personalized medicine, cancer genomics, and systems biology modeling.
Course Schedule
Important Dates: Application open: June 5th, 2024 Application closes: Aug 19, 2024 First lecture: Aug 27, 2024, 08:00 CEST Hackathon: Oct 9-11, 2024 Final Project: Oct 7-27, 2024 Final Project consultation: Oct 17, 08:00-11:00 CEST Final Project Report submission deadline: Sunday, Oct 27, 2024, 23:59 CEST Oral Presentation (batch 1): Tuesday, Oct 29, 2024, 8:00-10:00 CEST (only for Master’s students) Oral Presentation (batch 2): Wednesday, Oct 30, 2024, 8:00-10:00 CEST (only for Master’s students) Lectures, Computer Labs, and Journal Clubs will be held online via Zoom.
Module 6 [1 ECTS] - Computational Modeling and Automated Scientific Discovery
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Final Project
Start date: Week 41 (7th October 2024) Final Project The final project allows students to showcase their learning by proposing their own dataset and project idea. This open-ended assignment encourages participants to explore topics of interest within data-driven life science and apply the skills gained throughout the course individually or in a pair (both members should be at the same level, e.
Module 3 [1 ECTS] - Bioinformatics and Metagenomics
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Module 1 [0.5 ECTS] - Introduction to DDLS, AI and ChatGPT
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DDLS Hackathon - A Code Festival for the SciLifeLab/DDLS Community
Hackathon Join us at the DDLS Hackathon, a vibrant code festival for the SciLifeLab/DDLS community! This hackathon is part of the 2024 DDLS course and participants will collaborate on their projects in the dynamic field of data-driven life science, supported by teaching assistants in a hands-on environment.
Module 2 [1 ECTS] - Advanced AI Techniques in Biological Systems
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Module 5 [1 ECTS] - Single Molecule Analysis and Tissue Atlases
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Module 4 [1 ECTS] - AI in Bioimage Analysis
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📊 HT2024: Data-driven Life Sciences
Course content of the Data-driven Life Sciences course offered in 2024.
Module 5: Multi-omics Data Analysis and BioImage Analysis and Deep Learning
Lecture by Wen Zhong (DDLS Fellow) and Estibaliz Gómez de Mariscal (PhD)
Module 3: Super-Resolution Microscopy and Analytics to Investigate Complex Cellular Processes and Diseases
Lecture by Juliette Griffié (DDLS Fellow) on 12th Sept, at 08:00-10:00.
Module 2: Generative AI and its Applications in Life Sciences
We will introduce the basics of generative AI and its potential in life science. We will also cover recent trend of using generative large-language models and practical skills on how to use ChatGPT for reading, writing, planning and code generation. Lecture by Wei Ouyang (DDLS Fellow) on 5th Sept.
Module 6: Model Construction and Features of Alphafold2
Lecture by Darko Mitrovic (PhD) on 3rd Oct.
Module 4: Computational Methods in Evolution and Biodiversity
We will join the Computational Methods in Evolution and Biodiversity workshop in Stockholm University, participate in lectures by Serge Belongie, Katie E. Lotterhos, Tobias Andermann (DDLS Fellow) et al. on 21st Sept. The computer lab will happen on the same day in the afternoon.
Module 1: Introduction to DDLS and machine learning
We will introduce the concept of data-driven life sciences and the data life cycle, the course and the different modules, and some basics about using ChatGPT. Lecture by Wei Ouyang on 29th August.
📊 HT2023: Data-driven Life Sciences
Course content of the Data-driven Life Sciences course offered in 2023.
Prerequisites
Be prepared As prerequisites for the course, we recommend that you have a look at the following resources: Please have a look at the SciLifeLab Data-Driven Life Science (DDLS) initiative website to understand what data-driven life sciences are, and how Sweden is investing in this area.
Course Schedule
Important dates Course start: 29 August 2023 Course registration deadline: 4 September 2023 Course end: 6 October 2023 Exam: 11 October 2023 & 25 October 2023 NOTE: The schedule is subject to change, please keep an eye on the email announcement and the updated schedule HERE (which may differ from the schedule at KTH course web).
Prerequisites
Be prepared As prerequisites for the course, we recommend that you have a look at the following resources: Please have a look at the SciLifeLab Data-Driven Life Science (DDLS) initiative website to understand what data-driven life sciences are, and how Sweden is investing in this area.
📊 HT2022: Data-driven Life Sciences
Course content of the Data-driven Life Sciences course offered in 2022.
Final DDLS Project
Final project plan and report for the DDLS course.
What Students Say
The DDLS course broadened my general knowledge in data-driven life sciences. Throughout the course, we were learning how to use AI tools effectively. I was surprised by how quickly I could build my first web app!
The course provided a solid introduction to applying agentic AI in both research and coding. The final project was especially useful as a chance to put the AI-supported coding workflows into practice.
This course was a great introduction to the world of data-driven life science research. It contained interesting lectures as well as hands-on experience working with Google Colab and Gemini CLI as an AI agent.
Meet the Team
Wei Ouyang
Teacher / Examiner
Nils Mechtel
Teaching Assistant
