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Data Science for Business Intelligence (DSBI)

Business intelligence refers to technologies, applications and practices for the collection, integration, analysis, and presentation of business data in order to support business decision making. Essentially, it is a collection of data-driven decision support models. Data science studies the computational principles, methods and processes for extracting information and knowledge from various types of data. It has been successfully used in many fields such as economics, finance, marketing, psychology, physics, and engineering, to provide insights into data as well as to support decision making. This course teaches students analytical skills on empirical data by introducing popular data science methods to support decision making and evaluation in business. It uses a combination of lectures and workshops. The course emphasizes the practical applications and makes extensive use of Python for business data visualization and analysis.   

Find full course description in the course catalogue.    


The grade for the course consists of two parts:

  • Coursework counting app. 40 %
  • 3-hour written exam counting app. 60 %

The written exam is online and presence in Aarhus is not nescessary.

Admission Requirements

Course specific:

To apply for the course you must either be enrolled in a bachelor's degree, have a bachelor's degree or have passed a qualifying entry examination.

The course is designed as an introductory course for third-year undergraduates in social science. Although the course is self-contained and fundamental mathematics will be reviewed, students are expected to have basic mathematics knowledge (i.e., calculus and linear algebra).  


Exchange students: nomination from your home university

Freemovers: documentation for English Langauge proficiency

You can read more about admission here


Bowei Chen


Academic profile


Bowei Chen is Lecturer in Marketing at the University of Glasgow. His research interests include Data Science, Machine Learning, Digital Marketing, Quantitative Finance and Information Systems.