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2026/2027  MA-MMBDV2603U  Business Data Analytics

English Title
Business Data Analytics

Course information
Language English
Course ECTS 5 ECTS
Type Elective
Level Part Time Master
Duration One Semester
Start time of the course Autumn, Spring
Timetable Course schedule will be posted at calendar.cbs.dk
Min. participants 10
Max. participants 30
Study board
Study Board for Master i forretningsudvikling
Programme Master of Business Development
Course coordinator
  • Raghava Rao Mukkamala - Department of Digitalisation (DIGI)
Main academic disciplines
  • Information technology
  • Organisation
  • Statistics and quantitative methods
Teaching methods
  • Face-to-face teaching
Last updated on 18-06-2026

Relevant links
Learning objectives
  • Identify and explain a business problem or opportunity with relevance to Business Data Analytics in your own organization and/or industry sector.
  • Demonstrate how the business data analytics course concepts, frameworks, methods, and tools can be used to analyze the business problem or opportunity.
  • Technically evaluate and critically reflect on the conceptual and practical implications of the business data analytics.
Course prerequisites
Students should have basic knowledge of management and experience with Excel.
The course target managers and specialists working with, or interested in working with, external and internal data for different business functions such as Customer Segmentation, Human Capital Analytics, Predictive Maintenance etc. The course is also open for professionals who would like to understand the power of analytics and get knowledge about how to use data for evidence-based management decisions.
Examination
Business Data Analytics:
Exam ECTS 5
Examination form Home assignment - written product
Individual or group exam Individual exam
Size of written product Max. 10 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.
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Winter and Summer
Make-up exam/re-exam
Same examination form as the ordinary exam
Course content, structure and pedagogical approach

This course provides participants with an in-depth exploration of business data analytics and its potential to generate competitive advantages. Through a combination of theoretical frameworks and practical tools, you will learn how to turn big data sets into strategic assets that can drive decision-making and enhance business processes.

Key topics include three paradigms for leveraging new technologies, a framework for transforming data into business value, and practical applications of visual analytics, text analytics, and predictive analytics. A pilot project on your own company’s data (or on a synthetic datasheet if your company’s data is unavailable) will provide hands-on experience in applying the concepts learned. The course combines conceptual knowledge with practical insights, preparing you to implement data-driven solutions in your organisation.

 

Your benefits

  • Identify and explain a business problem or opportunity relevant to Business Data Analytics in your organisation or industry
  • Apply course concepts, frameworks, methods, and tools to analyse business challenges using business data analytics
  • Critically evaluate the conceptual and practical implications of business data analytics and its real-world applications
  • Develop and communicate actionable insights from big data to drive business value and digital transformation

 

Key themes

  • Conceptual Framework for Business Data Analytics
  • Framework for Value Generation from New Technologies
  • Methods and Tools for Visual Analytics and Predictive Analytics
  • Pilot Project on your own company’s business data set.
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
  • Methodology
  • Models
Research-like activities
  • Data collection
  • Analysis
  • Discussion, critical reflection, modelling
  • Students conduct independent research-like activities under supervision
Description of the teaching methods
Blended Learning, Interactive lectures, Hands-on exercises, “Show-and-Tell” Demos, and In-Class Project Work.
Feedback during the teaching period
Feedback during class is possible.
Student workload
Preparation and exam 109,5 hours
Teaching 28 hours
Last updated on 18-06-2026