Vom Abakus bis zu ChatGPT hat sich unsere Fähigkeit, Daten zu erzeugen, aber auch die Art und Weise, wie wir sie analysieren, im letzten halben Jahrhundert erheblich verändert. Ziel dieses Kurses ist es, die Geschichte der ursprünglichen Datenerfassung und -nutzung zu erforschen, einige der wichtigsten Auswirkungen dieser Wissenschaft zu erörtern und Beispiele aus der Praxis zu geben, wie man das Wissen in der Informationsflut, die unsere Welt überschwemmt, finden kann. Die Teilnehmer sollten im Laufe des Seminars eine Kombination aus Vorlesungen und praktischen Übungen erwarten. Sowohl die Vorlesung sowie auch die Übungen werden einen Überblick über die Geschichte der für die Datenanalyse verwendeten Werkzeuge vermitteln. Offene Diskussionen sind ebenfalls erwünscht, da in der Vorlesung wichtige, die Gesellschaft verändernde Themen in Bezug auf die damaligen Informationen und deren Verwendung behandelt werden.

-- This course will be taught in English -- This seminar series is a collaboration between the Institute of Biomedical Informatics (BI-K) and the University and City Library (USB). This seminar series introduces students to the principles and practices of inter- and transdisciplinary (ITD) collaboration in the development of digital solutions, across areas such as research, healthcare, industry and society. As digital transformation and complex challenges increasingly require cooperation between diverse fields, such as computer science, engineering, medicine, the social sciences, and the humanities, students will learn how to navigate constant change and contribute effectively to cross-disciplinary teams. Each session combines theory with real-world examples and focuses on communicating across different fields, co-designing solutions, responsible innovation and open science practices. The series emphasizes project-based learning, and hands-on communication strategies for building shared understanding and actionable outcomes in complex digital contexts.

This course offers an introduction to the fundamentals of blockchain technology and its potential applications in the healthcare sector. Using concrete examples from medical practice, the course covers key concepts such as smart contracts, data protection, and the traceability of pharmaceuticals. The course is designed for medical students with no prior knowledge of computer science or programming.

The course aims to highlight the challenges posed by the recent explosion of AI applications. Topics to be covered include artificial agents, privacy and consent, bias and discrimination, the environmental and social aspects of AI applications, and guidelines and frameworks for ethical, trustworthy, and responsible AI. It is online, in English, assumes no prior knowledge of ethics or AI, and is assessed through presentations based on scientific publications selected by the students in collaboration with the tutor.

[This course is offered in English] Generating knowledge from data using machine learning (ML) is becoming increasingly important in every conceivable scientific field. To provide an introduction to data science, this course will cover various ML methods, including supervised and unsupervised methods, as well as techniques for evaluating and visualising the results.With a focus on practical implementation, all approaches presented will be briefly introduced theoretically and then implemented using the programming language python.Prior knowledge of programming is not required. The first lecture will cover a python demo. To pass the course the students have to apply the introduced methods in an own data science projects and present their results in a 5-10 minute presentation (depending on the number of participants). The projects and presentations will not be graded but have to meet the requirements presented in the lecture.