Course content
This course is designed to equip students with practical knowledge of tools and techniques for the exploration, analysis and visualization of data in business. It also deals with conceptual, societal and ethical issues associated with these techniques. Thus it addresses several key aspects of the Nordic Nine -- especially under Knowledge ("analytical with data and curious about ambiguity") and under Values ("understand ethical dilemmas and have the leadership values to overcome them").
The course has a blended format, with some online activities, including quizzes and online discussion groups. In addition, there will be regular hands-on lab sessions. The course includes an independently chosen project, which will take the form of a business case analysis. Students will select a dataset, to which they apply data science techniques, building relevant models and assessing them from a business and data science perspective.
The course will cover the following main topic areas:
- Basic techniques for analysis of structured data, including use of query languages
- Basic machine learning tools and techniques, including classification and regression, as well as unsupervised methods such as clustering
- Techniques for visualization and presentation of the results of data analysis
- Conceptual, societal and ethical issues with business data analytics
Students are expected to work with large language models and other forms of
generative AI in exercises, assignments, and exams. As with any other software, it should be clearly stated how the AI models are used in the performance of a given exercise, assignment, or exam.
See course description in course catalogue