Web Science aims at understanding the interrelationship between networks, humans and society, and technology and how the micro-processes that underlie each of the three building blocks and happen between them lead to the macro-phenomena that shape how we perceive the Web today.
Web Science studies how the web has developed and how it affects its stakeholders by deliberately taking into account the views of different disciplines (from computer science to psychology). It is the interactions of humans and technologies which shapes the appearance of the web and determines its use. The research of the Web Science group contributes to a better understanding of the web by studying how researchers use the social web for scholarly communication and research evaluation (e.g. altmetrics, Science 2.0, and Open Science), how users and researchers can contribute to the advancement of knowledge and science by adding their own content (e.g., to library catalogues via folksonomies), why users and researchers use web functionalities and for what purposes, how digital libraries can use social media and user-generated content (e.g., for knowledge representation and information retrieval) as well as foster user engagement.
The master program covers a wide range of topics from Web Science and enables students to build proficient knowledge on web-based services (e.g., social media) or phenomena (e.g., filter bubble) as well as on typical methods used in Web Science (e.g., social network analysis).
It is obligatory to participate in the module "Social Media and Web Science". It is possible to attribute this module to "Wahlpflicht Informatik".
It is possible to attend a second Seminar - here it is recommended to choose Data Mining und Machine Learning auf sozialen Medien und offenen Daten (5 LP).
The Projekt Wirtschaftsinformatik will be offered on request. Please contact the Research Advisor.
Recommended courses in Wahlbereich Wirtschaftsinformatik: Quantitative Methods in Human-Computer Studies (6 LP), Informations- & Wissensmanagement (8 LP), Projektmanagement (6 LP), Data Mining and Machine Learning: Basic and Advanced Techniques of Data Analysis (8 LP), Web Information Retrieval (6 LP), E-Commerce (8 LP), E-Business und e-Marketing (6 LP)
Recommended courses in Wahlbereich Informatik: XML in Communication Systems (8 LP), Web-Technologien (5 LP), Semantic Web and Linked Open Data (6 LP), Software Architecture (6 LP), Ausgewählte Themen der Künstlichen Intelligenz (6 LP)
Recommended courses in Grundlagen & Umfeld: Bibliometrie und Altmetrics (6 LP), Basiskompetenzen (6 LP)
Most of the lectures and courses will be offered in German language, but English skills (oral and written) might be necessary for some of them.
This master program is designed to give you high flexibility while still allowing you to conduct a thesis advised by the Research Advisor. As we do not expect you to choose your Research Advisor right at the beginning of your master studies, it is possible to enter/leave this program even after you have started your master studies. However, it is recommended you choose your Research Advisor by the beginning of the final year of your master studies.
| WS17/18 | WInf-Msc-SemInc | WInf-DMML4 | WInf-Projma | WInf-BibAlt | Inf-SoftArch |
| SS18 | WInf-SMWS | Inf-TGI | |||
| WS18/19 |
| Wahlpflicht Wirtschaftsinformatik | |
| WInf-DMML4: Data Mining and Machine Learning: Basic and Advanced Techniques of Data Analysis (8 ECTS, WS17/18, empfohlen) | |
| WInf-Projma: Projektmanagement (6 ECTS, WS17/18, empfohlen) | |
| Seminar Wirtschaftsinformatik | |
| WInf-Msc-SemInc: Master Seminar - Incentives für Collective Intelligence und User Generated Content (5 ECTS, WS17/18, Pflicht) | |
| Projekt Wirtschaftsinformatik | |
| Wahlpflicht Informatik | |
| Inf-TGI: Theoretische Grundlagen der Informatik (8 ECTS, SS18, Pflicht) | |
| Inf-SoftArch: Software Architecture (6 ECTS, WS17/18, empfohlen) | |
| Wirtschaftswissenschaften | |
| Grundlagen und Umfeld | |
| WInf-SMWS: Social Media und Web Science (6 ECTS, SS18, Pflicht) | |
| WInf-BibAlt: Scholarly Big Data Analysis (6 ECTS, WS17/18, empfohlen) |