After this course, students should be able to:
1. Explain and work with the basic concepts of several structured and unstructured data types
2. Explain and understand existing models and methods to analyse structured and unstructured data types published in the academic literature
3. Evaluate existing models and methods published in the academic literature
4. Identify suitable methods to analyse structured and unstructured data types
5. Estimate a suitable model using empirical data and statistical software
6. Interpret an estimated model, and draw managerial implications
7. Develop their own models and provide interpretations thereof based on the learned methods and available data

PLEASE NOTE THAT THE INFORMATION ABOUT THE TEACHING AND ASSESSMENT METHOD(S) USED IN THIS COURSE IS WITH RESERVATION. A RE-EMERGENCE OF THE CORONAVIRUS AND NEW COUNTERMEASURES BY THE DUTCH GOVERNMENT MIGHT FORCE COORDINATORS TO CHANGE THE TEACHING AND ASSESSMENT METHODS USED. THE MOST UP-TO-DATE INFORMATION ABOUT THE TEACHING/ASSESSMENT METHOD(S) WILL BE AVAILABLE IN THE COURSE SYLLABUS.

The digitalization of business has increased the amount of data available within organizations. Now firms and managers are faced with the task of creating insights from these new and increasing (in volume) sources of data. Challenges arise when we consider the nature of some these new forms of data. One issue we face is that data becomes increasingly unstructured (e.g., text, visual and audio data), which requires different methods of analysis compared to classical (structured) data forms. Beyond that, thinking about how we can use such unstructured data in day-to-day business operations is also not apparent. Only when we are able to process these data and link them to business relevant outcomes can firms and managers benefit from new insights and can data create business value. This is the main focus of the course Analysing Unstructured Data. In this course, students will learn how to work with unstructured data forms (i.e., text, visual and audio data), and become familiar with applications of these data types to business problems to understand how they can be used to inform managerial decision making. Weekly group assignments equip students with the R skills to perform analyses on unstructured data themselves.

A selection of articles/book chapters will be made available.

Experience in R, such as gained in the course Business Analytics. Prior experience in business modelling and statistics is highly recommended (e.g. obtained in courses such as Business Analytics and/or Descriptive and Predictive Analytics)