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Industrial Data Science (InDaS) – Qualification concept for machine learning in industrial production

(Project duration: to Oktober 2019 )


Problem Defintion

Regarding Industrie 4.0 the dissemination of modern information and communication technologies as well as the technological ability to systematically and comprehensively record and store data, allows to build up dynamic information memories of so far unknown size and quality. The interpretation and efficient use of implicit knowledge in data to support decision-making and planning is increasingly becoming the focus of manufacturing companies. Methods of Six Sigma, Machine Learning and Digital Factory provide a selection of process approaches for the intelligent and automated evaluation of large amounts of data. The fusion of these methods into a holistic approach ensures to identify and visualize previously unknown interrelationships, to check existing a priori knowledge and to use it for predictive prognosis. The knowledge gained in this way, combined with the practical experience of the employees, will forthcoming form an essential success factor for companies.

Against this background, the company-specific use and application of data analysis methods is attracting increasing attention. Successful and efficient processing of industrial application cases requires both methodological know-how in the field of statistics and computer science and, due to the often technical nature of the application cases, domain sound knowledge in engineering science. However, companies often lack sufficient expertise in the cross-section of these specialist areas to meet these challenges. Qualified personnel, who master the use of machine learning methods, know the special requirements of companies, as well as have sufficient domain knowledge to understand and successfully solve engineering-scientific applications, is rarely available.

In the training of young academics and in the further training of skilled workers, this problem is rarely or insufficiently addressed. Training in the field of data science is generally subject-specific and therefore focuses predominantly on theoretical content, but does not take into account practical feasibility or the special framework conditions of industrial practice.


The aim of the planned research project is therefore the development of an innovative teaching concept for the qualification of young academics and specialists from industry in the field of industrial data science (cf. figure). In particular, the special challenges of manufacturing companies are to be taken into account and the necessary skills for recording and handling industrial application cases for the development of (partially) automated solution processes are to be imparted to the participants.

The teaching concept is to integrate two essential and innovative features: On the one hand a practice-oriented knowledge transfer in the field of Industrial Data Science to solve real problems in manufacturing companies and on the other hand learning in heterogeneous groups of students of statistics, computer science and engineering as well as specialists of industry. The concept thus meets the requirements of the highest possible practical relevance, as well as the promotion of an intensive, interdisciplinary exchange. In order to realize the planned research goals, a two-part training program is to be developed, which will initially focus on teaching the theoretical contents of Industrial Data Science and then evaluate the application of the theory using practical application scenarios from industry as examples.



Research and Development Partners:

  • Chair of Computer-Aided Statistics, TU Dortmund University
  • Chair VI Databases and Information Systems, TU Dortmund University
  • Institute for Production Systems, TU Dortmund University
  • Department of Machine Learning, Darmstadt University of Technology


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Secretariat IPS
Tel.: 0231 755-2652

Promotional Note

This research and development project is funded by the German Federal Ministry of Education and Research (BMBF) as part of the "IKT 2020 - Research for Innovation" programme and is supported by the project management agency Deutsches Luft- und Raumfahrtzentrum e.V. (German Aerospace Center) (DLR).



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