=Paper=
{{Paper
|id=Vol-1968/keynote
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-1968/keynote.pdf
|volume=Vol-1968
}}
==None==
<pdf width="1500px">https://ceur-ws.org/Vol-1968/keynote.pdf</pdf>
<pre>
Deep Learning and Economical Applications

                               Kai Heinrich

                                TU-Dresden
                        kai.heinrich@tu-dresden.de



 Abstract. Facing the digitaization caused through disruptive technolo-
 gies, the filed of data science is broadened, especially through the use
 of deep learning techniques. Not only can those deep networks help to
 successfully solve regression and classification problems but also can gen-
 erate content like game theoretic decisions and are therefore able to help
 simulate game theory approaches. However this development requires the
 a data scientist to have a more in-depth knowledge as well as a substan-
 tial technical background. We show how the development of economic
 data scientist has changed and propose a set of skills generated from
 multiple empirical sources like expert interviews, web mining as well as
 surveys in a meta analysis. We also distinguish the concept of big data
 from the concept of data science.

 Keywords: Economics, Data Science, Big Data, Deep Learning

</pre>