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2026/2027  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
Programme BSc in Digital Management
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 17-06-2026

Relevant links
Learning objectives
After the course, the student will be able to:
  • Show the ability to apply techniques for exploring and analyzing structured data, and use them in practical applications with real-world datasets.
  • Apply basic machine learning techniques for classification and regression; build models and analyse and evaluate machine learning models, based on standard metrics and machine learning principles.
  • Present results of data analytics using a variety of visualisation techniques, based on standard tools. Visualisations should be developed according to conceptual principles of visualisation. Analyze and assess visualisations based on these principles.
  • Synthesize techniques for data analysis, modeling and visualisation in the context of business and societal goals. Evaluate results from business, societal and ethical perspectives.
Examination
Business Data Analytics and Visualization:
Exam ECTS 7,5
Examination form Active participation

The completion of this course is based on active student participation in class. The course will be considered as passed if the students participation - based on an overall assessment - in the class activities fulfill the learning objectives of the course. The individual student’s participation is assessed by the teacher.
The student must participate in A combination of assignment and presentation, Assignment(s)
Individual or group exam Individual exam
Grading scale Pass / Fail
Examiner(s) Assessed solely by the teacher
Exam period Summer
Make-up exam/re-exam 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.
Size of written product: Max. 15 pages
Assignment type: Project
Duration: 15 min. per student, including examiners' discussion of grade, and informing plus explaining the grade
Examiner(s): If it is an internal examination, there will be a second internal examiner at the re-exam. If it is an external examination, there will be an external examiner.
Description of activities
A combination of assignment and presentation: Students will do a project either in a group or individually. They will write a paper and make a presentation of their results at the end of the semester.
Assignment(s): Students will present a project plan at the middle of the semester. Also students will do weekly quizzes and group discussions where they post the results of the discussion. They also participate in weekly lab sessions where they submit the results of the lab.
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
Weekly exercises involve programming tasks connected to course topics, such as classification, regression, data analysis and visualisation. Students receive feedback on their work. There are also weekly in-class quizzes and group discussions with submitted results. Students also receive informal feedback on preliminary plans for a course project.
Student workload
Lectures: group discussions and quizzes 30 hours
Readings and class preparation 106 hours
Final project: write paper and make oral presentation of project 40 hours
Activity sessions 30 hours
Further Information

Make-up exam/re-exam questions will be published on Digital Exam at the beginning of the exam, and the written project should be submitted by a specified date and time.
Expected literature

Andreas, C. (2017). Miller, Sarah Guido. Introduction to Machine Learning with Python-O'Reilly Media.
Last updated on 17-06-2026