* Understanding and using academic knowledge: Students are able to apply knowledge of frameworks, approaches, perspectives, and methodologies relevant for the analysis of business and economic data. Students are able to apply this knowledge and develop their own innovative ideas in the context of an actual business or economic problem.
* Academic attitude: Students are able to reason in an academic manner and critically reflect on the possibilities and limitations of methods and data. In addition, they are able to translate a general problem into specific (research) questions, and are able to collect and pre-analyze data to prepare for an in-depth research. Finally, students are able to show great independence in acquiring the necessary knowledge and skills.
* Interpersonal competences: Students are able to communicate the outcomes of their work. Furthermore, they are able to collaborate effectively in a research team and manage projects in their teams successfully.

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.

Research project is about developing and implementing in teams of 4-5 students a solution strategy to solve a business or economic problem. The problems are connected to research subjects of members of the department Data Analytics and Digitalization, who will also supervise the teams working on their subject. Each problem comes with data. Narrowing down a broad managerial or economic question to specific subproblems that can be answered by applying data science techniques and insights from the curriculum on the data is the first step of the project. Identifying, understanding, and judging relevant academic literature to solve the subproblems is the second step. Literature can include literature on data science methodologies as well as literature on business, economic and other scientific knowledge of the subject area. Data cleaning, additional data collection, and preliminary data analysis is also an essential component. The final product is a research agenda, broken down into well-defined work packages that form the topics for individual bachelor theses of the team members.