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Courses offered in SS 22

  • Lecture: Bridge Course Databases

  • A blended learning bridge course for master students in Data Science, Computational Social Science and related programs.

  • Lecture: Social Computing

  • Social Computing is an area of computer science that is concerned with the intersection of social behavior and computational systems. It is based on creating or recreating social conventions and social contexts through the use of software and technology. We will address social computing infrastructures, social computing engineering processes, computational social science, in particular recommender systems and community detection, crowdsourcing, collective intelligence, the dark web, mixed reality, mobile social computing, science 2.0 and advanced topics.

  • Proseminar: Algorithmen für die Entdeckung von Communities in sozialen Netzwerken

  • In diesem Proseminar werden sogenannte Overlapping Community Detection Algorithms (OCDA) mittels eines multi-perspektivischen Kriterienkatalogs untersucht. Neben klassischen informatischen Kriterien wie Korrektheit, Laufzeit und Speicherplatzverbrauch werden Kriterien wie Genauigkeit und Güte der gewonnen Information, aber auch die Anwendbarkeit auf bestimmte Formen sozialer Netzwerke (assoziativ und dissassoziativ) eingesetzt. Die Bewertungen werden beispielsweise durch Spinnendiagramme visualisiert. Das Proseminar bietet neben der üblichen Einführung in das wissenschaftliche Arbeiten spannende neue Formen des kollaborativen Forschen und Publizieren auf dem Web geübt. So werden die fachlichen Themen in Zweiergruppen mittels einer Wiki-Buch Plattform erarbeitet. Zusätzlich werden Gruppen zum fachlichen Begutachten, zum Web-Design und zur Animation von Algorithmen gebildet. Die Ergebnisse des Proseminars werden daher nicht in einer Schublade verschwinden, sondern auf dem Web als Open Content publiziert.

  • Seminar: Web Science Seminar

  • Web Science has become an interdisciplinary study field between computer science, mathematics, sociology, economics, and other disciplines. This seminar researches advanced Web Analytics and Web Engineering topics in Web Science probably leading to master thesis topics for excellent students. Topics include: network evolution models and network dynamics, (overlapping) community detection, recommender systems, adaptation and personalization in Web Environments, the Educational Web, Web Trust & Credibility, Web Protocols, Peer-to-Peer Networking for Web Clients, Web-based Software Development Models, particular Web Development methods like Web Components and many more. Students do not only learn to write and present scientific papers but also to peer review them. Students will be assigned to a supervisor helping the student through all steps like literature research, seminar paper and seminar presentation.

  • Seminar: Data Science in Medicine

  • Health data analytics is one of the main drivers for the future of medicine. Various sources of big data, including patient records, diagnostic images, genomic data, wearable sensors, are being generated in our everyday life by health care practitioners, researchers, and patients themselves. Data science aims to identify patterns, discovering the underlying cause of diseases and well being by analyzing this data.

  • Practical course (basic level): Mixed Reality Lab

  • Mixed Reality is a continuum of spatial computing experiences on virtual, augmented and extended reality devices, such as the Microsoft HoloLens, the HTC Vive, and mobile phones. In this lab, we learn the basics of mixed reality software development in hands-on lessons with practical tasks. The lab contains a small independent project student groups can propose and work on.

  • Practical course (basic level): Basic End2End Resourcemanager

  • In this practical course, the participants learn to run a software development project and create a software product from the very beginning - from requirement analysis to release. The students will learn the importance of Scrum as part of the agile software development process.

  • Practical course (advanced level): Sovereign Data Exchange

  • In this lab, we will apply these technologies to some data exchange/data sharing scenarios. Students are expected to develop a complete workflow for a data exchange, including data preparation, policy definition, apps for enriching data, etc.