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- 12:30 - 13:00 (CEST): Qualification of future Industrial Data Scientists
- 13:00 - 13:30 (CEST): Targeted development of competencies for ML in SMEs
- 13:30 - 14:00 (CEST): Competence assessment and development in the field of IDS
Qualification of future Industrial Data Scientists
Successful and efficient processing of industrial application cases requires methodological know-how in the field of statistics and computer science and, due to the technical nature of the application cases, domain knowledge in engineering. 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 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 presented qualification concept meets these requirements by a practice-oriented knowledge transfer in the field of Industrial Data Science to solve real problems in manufacturing companies with learning in heterogeneous groups of students of statistics, computer science and engineering as well as specialists of industry.
- IPS-Projektdetail page
- Federal Ministry of Education and Research (BMBF):
- German Aerospace Center (DLR) – Project Management Agency:
Targeted development of competencies for ML in SMEs
The constantly ongoing digitalization is changing the business environment of manufacturing companies at a rapid pace. The widespread dissemination of digital technologies is leading to the collection and storage of vast amounts of data. Targeted evaluation and use of these data stores by means of machine learning (ML) opens up previously unknown potential for knowledge acquisition. However, technical development is advancing at such a rapid pace that new challenges are constantly arising with regard to the development of employee skills. Especially the evaluation of the possibilities of ML as well as the application of data-driven methods for the solution of problems in production and assembly are coming to attention. This affects both plant users and plant manufacturers, who are under increasing pressure to offer ML-based services with their production plants. A growing tendency is therefore to outsource such activities to external service providers, thus giving the future commodity of data out of the hands of the company. In order to maintain and secure digital sovereignty, however, it is necessary to develop competencies within the manufacturing companies in order to maintain the sovereignty over their own data.
Competence assessment and development in the field of IDS
This presentation is about the fourth work package of the research project AKKORD, which is about the creation of a digital learning platform for industrial data analysis. In the following, an impression of the development process of the learning platform is presented and a first version of the learning platform is shown. The process of creation and conception is based on the KDD process and considers different requirements for digital learning platforms as well as the recording of competences and target groups in the form of personas for an optimal competence development. The learning content is divided into a basic course - which is presented in more detail today - and an advanced course, which will be based on the CRISP-DM process in the near future.