@article{GrillenbergerRomeike2015,
  author    = {Grillenberger, Andreas and Romeike, Ralf},
  title     = {Teaching Data Management},
  series = {KEYCIT 2014 - Key Competencies in Informatics and ICT},
  journal   = {KEYCIT 2014 - Key Competencies in Informatics and ICT},
  number    = {7},
  publisher = {Universit{\"a}tsverlag Potsdam},
  address   = {Potsdam},
  issn      = {1868-0844},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-82648},
  pages     = {133 -- 150},
  year      = {2015},
  abstract  = {Data management is a central topic in computer science as well as in computer science education. Within the last years, this topic is changing tremendously, as its impact on daily life becomes increasingly visible. Nowadays, everyone not only needs to manage data of various kinds, but also continuously generates large amounts of data. In addition, Big Data and data analysis are intensively discussed in public dialogue because of their influences on society. For the understanding of such discussions and for being able to participate in them, fundamental knowledge on data management is necessary. Especially, being aware of the threats accompanying the ability to analyze large amounts of data in nearly real-time becomes increasingly important. This raises the question, which key competencies are necessary for daily dealings with data and data management. In this paper, we will first point out the importance of data management and of Big Data in daily life. On this basis, we will analyze which are the key competencies everyone needs concerning data management to be able to handle data in a proper way in daily life. Afterwards, we will discuss the impact of these changes in data management on computer science education and in particular database education.},
  language  = {en}
}
@article{GrillenbergerRomeike2018,
  author    = {Grillenberger, Andreas and Romeike, Ralf},
  title     = {Was ist Data Science?},
  series = {Commentarii informaticae didacticae},
  journal   = {Commentarii informaticae didacticae},
  number    = {10},
  publisher = {Universit{\"a}tsverlag Potsdam},
  address   = {Potsdam},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-416369},
  pages     = {119 -- 134},
  year      = {2018},
  abstract  = {In Zusammenhang mit den Entwicklungen der vergangenen Jahre, insbesondere in den Bereichen Big Data, Datenmanagement und Maschinenlernen, hat sich der Umgang mit Daten und deren Analyse wesentlich weiterentwickelt. Mittlerweile wird die Datenwissenschaft als eigene Disziplin angesehen, die auch immer st{\"a}rker durch entsprechende Studieng{\"a}nge an Hochschulen repr{\"a}sentiert wird. Trotz dieser zunehmenden Bedeutung ist jedoch oft unklar, welche konkreten Inhalte mit ihr in Verbindung stehen, da sie in verschiedensten Auspr{\"a}gungen auftritt. In diesem Beitrag werden daher die hinter der Data Science stehenden informatischen Inhalte durch eine qualitative Analyse der Modulhandb{\"u}cher etablierter Studieng{\"a}nge aus diesem Bereich ermittelt und so ein Beitrag zur Charakterisierung dieser Disziplin geleistet. Am Beispiel der Entwicklung eines Data-Literacy-Kompetenzmodells, die als Ausblick skizziert wird, wird die Bedeutung dieser Charakterisierung f{\"u}r die weitere Forschung expliziert.},
  language  = {de}
}