Applied Data Science in Europe
Stadelmann, Thilo; Stockinger, Kurt; Braschler, Martin; Cieliebak, Mark; Baudinot, Gerold; Dürr, Oliver; Ruckstuhl, Andreas (2013). Applied Data Science in Europe: Challenges for Academia in Keeping Up with a Highly Demanded Topic. European Computer Science Summit Peer reviewed.
Google Trends and other IT fever charts rate Data
Science among the most rapidly emerging and promising fields
that expand around computer science. Although Data Science
draws on content from established fields like artificial
intelligence, statistics, databases, visualization and many more,
industry is demanding for trained data scientists that no one
seems able to deliver. This is due to the pace at which the field
has expanded and the corresponding lack of curricula; the
unique skill set, which is inherently multi-disciplinary; and the
translation work (from the US web economy to other ecosystems)
necessary to realize the recognized world-wide potential of
applying analytics to all sorts of data.
In this contribution we draw from our experiences in
establishing an inter-disciplinary Data Science lab in order to
highlight the challenges and potential remedies for Data Science
in Europe. We discuss our role as academia in the light of the
potential societal/economic impact as well as the challenges in
organizational leadership tied to such inter-disciplinary work.