Industrial Engineering (IE) plays a pivotal role in enhancing productivity through optimization of both direct, value-adding processes and indirect, supporting processes within product development and manufacturing. However, in industry as well as academia, current IE methodologies fall short in addressing the increasing complexity and flexibility demands resulting from product variety, customization, and mixed manufacturing environments. This paper introduces a qualification concept designed to integrate data science methodologies into IE, utilizing a learning factory environment. The pedagogical framework uses Bloom’s taxonomy to accommodate diverse target groups, including undergraduates, postgraduates, and industry professionals. The theoretical foundation of this concept is based on the product development and production process as well as the Cross-Industry Standard for Data Mining (CRISP-DM). Data science methods are implemented in each phase of the product development and production process to bridge the gap between theoretical knowledge and practical application. A key component of the concept is illustrated through a use case on a data-driven planning process of screw fastening operations within a learning factory environment, where participants can apply data science techniques. An experiential learning platform allows learners to interact with tools and resources, enabling participants to actively engage with the material provided on GitHub. In conclusion, this paper showcases the potential of data science within the context of IE by addressing real-world industry challenges through a training program in a learning factory environment. Fostering a deeper understanding and practical proficiency in data science promises to equip future engineers with the skills necessary to navigate in an increasingly complex industrial landscape.