DBIS

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.

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

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

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.

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.

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

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.

More than twenty years after the birth of the World Wide Web, Web Science has been becoming a new 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.

More than twenty years after the birth of the World Wide Web, Web Science has been becoming a new 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.