Course content
Digital datasets contain digital traces of how certain stakeholders interact with a business model. These data therefore promise unique insights into markets, companies, and consumers. Digital data and other types of secondary data are often publicly available but come with a specific set of challenges. Specifically, digital datasets capture novel phenomena and are large, complex, and multifaceted, such that existing theories often provide insufficient guidance on how to analyze these data and interpret the results. Proficiency in digital data analytics—to source valid digital data, to successfully conduct meaningful analyses of these data, and to derive actionable managerial recommendations from the results—is therefore invaluable for academics and practitioners alike.
To develop students' digital data analytics skills, this course combines business research methods, philosophy of science, business model analytics, quantitative data analytics, and practical perspectives. Students work on a digital data analytics research project that is centered around a real-world business model. Three course phases train students in how to identify a research opportunity, explore its terrain, and advance understanding. Overall, this course aims to develop the skills required to successfully complete a research project that focuses on digital data analytics. Furthermore, the competencies gained in this course are directly relevant to students' other projects, including the bachelor project.
The course syllabus, distributed at the start of the course, provides additional details on the course topics, the course structure, and the course schedule.
See the full course description in the course catalogue