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2025/2026  BA-BDMAO2501U  Business Data Analytics and Visualization

English Title
Business Data Analytics and Visualization

Course information
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
Course coordinator
  • Daniel Hardt - Department of Management, Society and Communication (MSC)
Main academic disciplines
  • Information technology
  • Statistics and quantitative methods
Teaching methods
  • Blended learning
Last updated on 27-06-2025

Relevant links
Learning objectives
  • Understand techniques for exploring and analyzing structured data, and use them in practical applications with real-world datasets.
  • Understand basic machine learning techniques for classification and regression, including building models and applying basic principles for interpreting the outputs of machine learning models.
  • Understand and deploy techniques for visualizing and presenting results of data analytics. This includes both conceptual principles behind visualisations, and the use of practical tools to produce visualisations.
  • Demonstrate an analytical understanding of business, societal, and ethical issues in the application of data analysis techniques
Examination
Business Data Analytics and Visualization:
Exam ECTS 7,5
Examination form Oral exam based on written product

In order to participate in the oral exam, the written product must be handed in before the oral exam; by the set deadline. The grade is based on an overall assessment of the written product and the individual oral performance, see also the rules about examination forms in the programme regulations.
Individual or group exam Oral group exam based on written group product
Number of people in the group 2-4
Size of written product Max. 15 pages
Assignment type Written assignment
Release of assignment An assigned subject is released in class
Duration
Written product to be submitted on specified date and time.
15 min. per student, including examiners' discussion of grade, and informing plus explaining the grade
Grading scale 7-point grading scale
Examiner(s) Internal examiner and second internal examiner
Exam period Summer
Make-up exam/re-exam
Same examination form as the ordinary exam
If the student has participated in the written group project for the ordinary exam, but didn't attend the oral exam, the re-examination is conducted on the basis of the group project that has already been handed in.
However, a copy of the project for the ordinary exam MUST be handed in for the re-exam within a specified time.

If the student has participated in the written group project for the ordinary exam, but not passed the oral exam, the re-exam is conducted on the basis of the project that has already been handed in. However, the student may choose to hand in a new, individual project within a specified time.

NB! The student must clearly state at the frontpage of the project, if the product is IDENTICAL to the project handed in for the ordinary exam, or if the student has chosen to hand in a NEW PROJECT.

If the student has not submitted the written group project for the ordinary exam, the student may participate in the oral re-examination, if the student hands in an individual project within a specified time.”
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:

  • 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.
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
  • Classic and basic theory
  • Teacher’s own research
  • Methodology
  • Models
Research-like activities
  • Development of research questions
  • Data collection
  • Analysis
  • Discussion, critical reflection, modelling
  • Activities that contribute to new or existing research projects
  • Students conduct independent research-like activities under supervision
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.
Student workload
Lectures 30 hours
Readings and class preparation 116 hours
Exam Project and Preparation for Exam 60 hours
Expected literature

Andreas, C. (2017). Miller, Sarah Guido. Introduction to Machine Learning with Python-O'Reilly Media.
Last updated on 27-06-2025