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Courses offered in WS 20/21

  • Lecture: Bridge Course Databases

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

  • Lecture: Web Science

  • More than thirty years after the birth of the World Wide Web, Web Science is an established study field in Computer Science. This course introduces fundamental concepts (web centralities & basic algorithms, network models and web engineering principles) of Web Science. We will learn fundamental algorithms for web page ranking like PageRank and HITS as well as community detection algorithms. In the engineering part we dig into scalable approaches like cloud computing and peer-to-peer partly based on Post-HTTP protocols like the XMPP and WebRTC are. We will learn about Web Services and their RESTful implementation. With the knowledge gained in the preceding chapters we can analyze and engineer advanced Web applications.

  • Lecture: Semantic Web

  • As part of the W3C Semantic Web initiative standards and technologies have been developed for machine-readable exchange of data, information and knowledge on the Web. These standards and technologies are increasingly being used in applications and have already led to a number of exciting projects (e.g. DBpedia, semantic wiki or commercial applications such as, OpenCalais, or Google's KnowledgeGraph). The module provides a theoretically grounded and practically oriented introduction to this area.

  • Lecture: Privacy Enhancing Technologies for Data Science

  • This lecture covers current research results in the area of Privacy Enhancing Technologies (PETs) which can be applied to Data Science. These PETs have the potential to enable a new generation of privacy-enabled services which are not focused on maximizing the collection of user data. We use a mix of recent book chapters and papers from conferences and journals of the last few years as primary source material.

  • Lecture: Implementation of Databases

  • The lecture introduces basic technologies of the realization of database systems. Beside the coarse architecture (e.g. layered architecture) detailed procedures for the solution of special database tasks (especially query analysis and transaction management) will be discussed. The concepts of implementation will be applied to classical (relational model) as well as to more recent data models (distributed, XML). In addition to necessary theoretical fundamentals, practical concepts will be introduced that allow database administrators the efficient tuning of databases.

  • Lecture: Data Driven Medicine

  • This course offers a project-oriented, multidisciplinary introduction to the basics of data driven medicine. Orientation, fundamental concepts, and methodological approaches are provided by lectures. In addition, the participants will also form small interdisciplinary teams including students of computer science as well as medical students in order to plan and implement an own project, which targets prediction or decision support generated from medical data.

  • Proseminar: Intelligente Mobilitätssysteme

  • In diesem Proseminar werden aktuelle Themen aus dem Bereich Informationssysteme in dem Anwendungsgebiet Mobilität diskutiert. Hierbei liegt der Fokus unter anderem auf den zugrundeliegenden Konzepten, Algorithmen oder Datenstrukturen. Es werden sowohl theoretische Themen z.B. im Bereich der Graphentheorie behandelt als auch konkrete Mobilitätskonzepte und ihre genutzten Technologien besprochen. Unter anderem werden Themen im Bereich des Routings, des autonomen Fahrens und der Analyse von Verkehrsnetzen bearbeitet. Die Bearbeitung des Proseminars erfolgt in Zweiergruppen.

  • 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: Social Computing Seminar

  • 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. In this seminar we explore recent topics in social computing like Social Bots, Fake News, Filter Bubbles, Socio-political campaigns, Shit & Candy Storms, Social Augmented and Virtual Reality, Gamification, Serious Games, Science 2.0

  • Seminar: Methods for Data Reusability

  • The Internet and digital technologies are transforming societies, business and research. In our digital age, digital single markets remove barriers to access the goods and services, Industry 4.0 connects devices and platforms to enable flexible manufacturing processes, data driven science and big data analytics radically change research and innovation. All these promising advancements are only possible with the availability of the machine interpretable and processable data.

  • Practical course (basic level): Knowledge Graphs Praktikum

  • Knowledge Graphs are large graphs used to capture information about the real world in such a way that is is useful for applications. In these data structures, there are all sorts of entities (for example, people, events, places, organizations, etc.). Knowledge Graphs are used by many organizations to represent the information they need for their operations. The most well-known example is Google, where a knowledge graph is used to enrich the search results. Also personal assistants, such as Amazon’s Alexa, Apple’s Siri and Google Now, as well as question answering systems such as IBM Watson, make use of knowledge graphs to provide information to their users.

  • 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 MS 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 (advanced level): High-tech Entrepreneurship and new Media

  • The lab course combines tutorials and guest lectures on the development of complex information products with practical experience in start-ups on specific and typical IT-related problems of the companies taking part in the lab. Integrated into the concept of this course is the development of presentation and other soft skills. Every winter term we offer projects from local and international start-ups.

  • Practical course (advanced level): Data Visualisation and Analytics

  • This course provides participants with a comprehensive and versatile toolbox of data visualisation and analysis methods, which can be transferred to a vast number of applications.

  • Practical course (advanced level): Blockchain Experience Lab

  • By the end of the experience lab on blockchain technology students will have an elaborated understanding of the concept of blockchains for distritbuted data and transaction management and its potential impact for the digitalization of processes and businesses.