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Industrial Data Science: Developing a Qualification Concept for Machine Learning in Industrial Production

Bauer, Nadja; Stankiewicz, Lukas; Jastrow, Malte; Horn, Daniel; Teubner, Jens; Kersting, Kristian; Deuse, Jochen; Weihs, Claus

Abstract:
The advent of Industry 4.0 and the availability of large data storage systems lead to an increasing demand for specially educated data-oriented professionals in industrial production. The education of these specialists should combine elements from three fields: Industrial engineering, data analysis and data administration. However, a comprehensive education program incorporating all three elements has not yet been established in Germany.
The aim of the acquired research project, titled “Industrial Data Science” is to develop and implement a qualification concept for Machine Learning based on demands coming up in industrial environments. The concept is targeted at two groups: Advanced students from any of the three mentioned fields (Mechanical Engineering, Statistics, Computer Science) and industrial professionals.
In the qualification concept the needs of industrial companies are considered. Therefore, a survey was created to inquire the use and potentials of Machine Learning and the requirements for future Data Scientists in industrial production. The evaluation of the survey and the resulting conclusions affecting the qualification concept are presented in this paper.

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Verlagsausgabe §
DOI: 10.5445/KSP/1000087327/27
Veröffentlicht am 16.09.2020
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Wirtschaftsinformatik und Marketing (IISM)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2018
Sprache Englisch
Identifikator ISSN: 2363-9881
KITopen-ID: 1000123699
Erschienen in Archives of Data Science, Series A
Band 5
Heft 1
Seiten P27, 14 S. online
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