Business Analytics
Teaching students of heterogeneous backgrounds posits an inherent challenge of education, especially at D-MTEC. Such differences often impede the learning experience and, hence, require a tailored educational offering: In the design of the course “Business Analytics” we specifically leverage the heterogeneous backgrounds of our students (for instance but not limited to degrees in mechanical engineering, electrical engineering, computer science, biology) to improve their learning experience around data-driven problems in business contexts. An educational offering tailored to the students’ backgrounds combined with a peer-learning-based design helps students to develop the skills they need while leveraging interdisciplinary backgrounds as an asset.
Keywords
Course description
Project description
The course aims to educate future executives in operating, as well as managing, data science technology in order to create value in businesses and organizations. We aim to provide students with both competences to successfully apply BA tools and the know-how to select and effectively implement highly relevant tools into organizations. Instead of deploying the traditional “one-fits-all” approach, learning objectives of the course are tailored to the individual needs and prevalent skills of students. For MSc students, the course focuses stronger on analytics and methodological skills (such as developing analytics prototypes for concrete business problems). Students with industry experience (e.g. MAS) are more strongly confronted with identifying challenges and opportunities for data science in businesses, as well as the conceptualization of implementation strategies.
This approach is accompanied by a peer-learning-based design in which MAS and MSc students collaborate and interact frequently in class in order to create synergies based on their interdisciplinary backgrounds and skills. As a result, all student gain a profound knowledge about the interface of analytics and business with a deepened (expert) focus on one of the two overlapping fields. This knowledge is not based on mere theoretical concepts, but generated by interdisciplinary projects, discussions and case studies. All students will therefore be able to effectively apply the skills learned throughout the BA course in their own organizations, start-ups or future career paths and thereby actively drive the digital transformation of our society.
Learning objectives:
The learning objectives of the BA course are divided into three major categories. The learning objectives are aligned with the students’ backgrounds and needs, so that we differentiate by the heterogeneous backgrounds among students and leverage this as follows:
1. Managerial aspects: Students are engaged in the design and implementation of processes to efficiently manage analytics-related projects in business. Furthermore, students should be able to identify and communicate applications for analytics that create value in
corporates and organizations.
2. Methodological challenges: Students gain an understanding of common methods for performing business analytics and can compare their different properties.
3. Practical implementation: Students apply BA methods based on real-world datasets.
Given the aforementioned differences among students, we redesigned business analytics education at D-MTEC and replaced the old "one-fits-all" course by an educational offering that is tailored to the different student bodies. In the actual implementation, we set up two different courses with separate course numbers for MAS and MSc students, respectively. This allowed us to customize the learning objectives according to the needs of the different student bodies and further tailor the form of final assessment. However, both courses are taught simultaneously to both student bodies. This is accompanied with frequent in-class exercises (i.e. cases) that were particularly designed, so that MSc and MAS students must interact. In the "business"-oriented cases, MAS students act as domain experts, make MSc students aware of the actual business problem and help them in formulating effective communication strategies that suit management. In the "analytics"-oriented parts of the course, MSc students take a leading role as they help in conveying algorithm knowledge via peer learning. Thereby, we leverage the prior “business” knowledge of MAS students concerning real management problems and the profound “analytics” understanding of MSc students. As final assessment, students had to conduct a BA project on a real-world case. Thereby, they had to apply the concepts learned in class and demonstrate their understanding of implications, challenges, and potential business values regarding BA. In accordance to the differing learning objectives between MAS and MSc students, the projects of MAS students had a primary focus on the managerial implications while the projects of MSc students had a stronger focus on the methodological part of BA.