TY - BOOK A1 - Huber, Florian T1 - Hands-on Introduction to Data Science with Python N2 - In today’s world, data is generated at an unprecedented pace, and our ability to harness it is changing the way we live, work, and even think. Data science, the interdisciplinary field that blends statistics, computer science, and domain-specific knowledge, empowers us to extract insights from this vast ocean of data. As data science becomes increasingly essential across various industries and sectors, there is a growing need for skilled professionals who can make sense of data and transform it into actionable information. This book is designed to give you a very broad and at the same time a very practical hands-on tour through the full spectrum of data science approaches KW - Data Science KW - Python (Programmiersprache) KW - Lehrmittel Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:hbz:due62-opus-49548 N1 - The DOI represents all versions, and will always resolve to the latest one. The online version of this book can be found here: https://florian-huber.github.io/data_science_course/book/intro.html All materials and source code to render the book can be found on GitHub: https://github.com/florian-huber/data_science_course PB - Zenodo ET - v0.21 ER - TY - CHAP A1 - Weiler, Tim A1 - Struzek, David A1 - Müller, Claudia A1 - Huldtgren, Alina A1 - Klapperich, Holger A1 - Grosskopp, Sabrina A1 - Fischer, Florian A1 - Osterheider, Angela A1 - Gaertner, Wanda T1 - 2nd International Workshop on Co-Creation of Hybrid Interactive Systems for Healthcare: Integrating Interdisciplinary and Interprofessional Perspectives in Healthcare Innovation T2 - Mensch und Computer 2024 - Workshopband N2 - Recent advancements in data science and AI-driven healthcare technologies are bringing up novel opportunities for innovations, such as personalized medicine, self diagnostic tools for everyday use, or hybrid healthcare models. However, the development of these technologies often overlooks the perspectives of patients and their families and socio-cultural surroundings, posing significant social, technological, and ethical challenges related to data bias, empowerment or surveillance, respectively. Bringing together interdisciplinary, interprofessional, and intersectoral collaboration in a systematic way seems to be a crucial element for adressing these issues and ensuring the meaningful integration of sensitive data and AI technologies into patient-centred healthcare arrangements. In this workshop, researchers and practitioners from diverse related disciplines, including HCI, AI, social and cultural sciences, healthcare, gerontology, etc., are invited to share their case studies on innovative health technologies and medical AI. Drawing from contextual best practices, as well as challenges and failures, the workshop organizers aim to collectively devise a systematic approach for co-designing and implementing telemedical innovations in real-world healthcare settings. KW - Künstliche Intelligenz KW - Data Science KW - Gesundheitswesen KW - Mensch-Maschine-Kommunikation KW - Telemedizin KW - Individualisierte Medizin KW - Human-centered Design Y1 - 2024 U6 - https://doi.org/10.18420/muc2024-mci-ws03-120 PB - Gesellschaft für Informatik e.V. ER -