Data Science projects aim to methodologically extract knowledge and value from data to
help organizations to improve performance. Dedicated process models are applied to …

Through knowledge extraction from data with various methods, Data Science (DS) allows
organizations to achieve improvements in performance. The execution of these projects is …

The ongoing digitization of online learning resources has led to a proliferation of
collaboration platforms for specific areas of application and disciplines. Simultaneously, the …

The significant increase in the amount of generated data provides potential for organizations
to improve performance. Accordingly, Data Science (DS), which encompasses the methods …

[EN] The digitization of learning resources has led to an increase in specialized
collaboration platforms across various fields, including the need for manufacturing …

Data Science projects fail more often than other projects. Many companies therefore still
avoid addressing complex data-driven questions. Seeking the reason for failure only in the …

An overview of common process models for the implementation of data science is presented
in this article. Since the development of KDD and CRISP-DM, the central ideas have been …

Data Science (DS) has gained increased relevance due to the potential to extract useful
insights from data. Quite commonly, this involves the utilization of Machine Learning (ML) …

Der Beitrag stellt anhand des Data Science Process Model (DASC-PM) und mittels einer
Fallstudie dar, wie die umfassend gestalteten Projektphasen, Aufgaben und …

Abstract The emergence of Industry 4.0 (I4. 0) technologies has significant implications for
small and medium-sized enterprises (SMEs) in the manufacturing sector. Current research …