2025/2026 BA-BDMAO2501U Business Data Analytics and Visualization
English Title | |
Business Data Analytics and Visualization |
Course information |
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Language | English |
Course ECTS | 7.5 ECTS |
Type | Mandatory |
Level | Bachelor |
Duration | One Semester |
Start time of the course | Spring |
Timetable | Course schedule will be posted at calendar.cbs.dk |
Study board |
Study Board for Service and Markets
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Course coordinator | |
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Main academic disciplines | |
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Teaching methods | |
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Last updated on 27-06-2025 |
Relevant links |
Learning objectives | ||||||||||||||||||||||||||
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Examination | ||||||||||||||||||||||||||
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Course content, structure and pedagogical approach | ||||||||||||||||||||||||||
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:
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. |
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Research-based teaching | ||||||||||||||||||||||||||
CBS’ programmes and teaching are research-based. The following
types of research-based knowledge and research-like activities are
included in this course:
Research-based knowledge
Research-like activities
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Description of the teaching methods | ||||||||||||||||||||||||||
A mixture of face to face lectures and online activities such as quizzes, group work, and practical exercises in hands-on sessions | ||||||||||||||||||||||||||
Feedback during the teaching period | ||||||||||||||||||||||||||
Students submit result of hands-on exercises each
week, and they receive detailed written feedback on their
submissions before the following session. Students also receive
informal feedback on preliminary plans for a course project.
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Student workload | ||||||||||||||||||||||||||
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Expected literature | ||||||||||||||||||||||||||
Andreas, C. (2017). Miller, Sarah Guido. Introduction to Machine Learning with Python-O'Reilly Media. |