Online tutorial on survival analysis for biomarker discovery

In biomedicine, survival analysis addresses time-to-event data to study outcomes like patient survival and treatment response, and supports biomarker discovery. Yet, teaching this analysis is often hindered by mathematical and programming barriers. We present a structured, hands-on tutorial that goes beyond a typical online guide—offering integrated video lectures, literature, quizzes, and practical exercises. Built around Orange Data Mining, an open and free no-code visual analytics platform, the tutorial covers key concepts such as censoring, Kaplan-Meier curves, group comparisons, and biomarker discovery through real-world datasets. Organized in four pedagogical units, it progresses from basic survival data analysis to gene and gene-set biomarker discovery. Designed for 2–3 hours of learning, it supports both individual study and classroom use, and was successfully tested with over 120 participants.Author summary: In teaching data science, one of the biggest challenges is engaging the audience. Hands-on learning is a powerful way to do this—after all, the field itself starts with data. Another key consideration is the choice of tools: for true accessibility, the lesson should flow smoothly, keeping the focus on core concepts rather than technical hurdles. This is especially important in introductory courses for learners coming from application domains such as biomedicine. With this in mind, we’ve created a straightforward, hands-on tutorial for survival analysis and biomarker discovery, designed for both self-learners and educators. The tutorial is short, intuitive, and includes videos, written notes, quizzes, and practical exercises. All materials—including the tutorial, datasets, and software—are free and ready to use.