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2025/2026  KAN-CGMAI3002U  Business Intelligence

English Title
Business Intelligence

Course information
Language English
Course ECTS 7.5 ECTS
Type Elective
Level Full Degree Master
Duration Summer
Start time of the course Summer
Timetable Course schedule will be posted at calendar.cbs.dk
Min. participants 30
Max. participants 60
Study board
Study Board for Governance, Law, Accounting & Management Analytics
Programme Master of Science (MSc) in Economics and Business Administration - General Management and Analytics (GMA)
Course coordinator
  • Raghava Rao Mukkamala - Department of Digitalisation (DIGI)
For academic questions related to the course, please contact course responsible Raghava Rao Mukkamala (rrm.digi@cbs.dk).
Main academic disciplines
  • Management
  • Marketing
  • Supply chain management and logistics
Teaching methods
  • Face-to-face teaching
Last updated on 03/11/2025

Relevant links
Learning objectives
By the end of this course students will be able to:
  • Explain the key concepts of business intelligence and the role of data-driven decision-making in business and society.
  • Identify and critically assess different types of analytics, data sources, and formats relevant for business challenges.
  • Process, summarize, and visualize business data using appropriate analytical tools and methods, while communicating insights effectively.
  • Apply a wide range of quantitative and machine learning methods to address complex business problems, balancing competitiveness with responsibility.
  • Evaluate and select suitable analytical approaches for specific business contexts.
  • Critically assess the ethical and societal implications of applying business intelligence in business and societal contexts.
Course prerequisites
This course is designed for graduate students in business disciplines such as information systems, marketing, operations management and analytics. A completed bachelor’s degree or equivalent is required. No prior knowledge of mathematics or programming is necessary, although students with basic mathematical or analytical skills are preferred.
Examination
Business Intelligence:
Exam ECTS 7.5
Examination form Home assignment - written product
Individual or group exam Individual exam
Size of written product Max. 10 pages
Assignment type Project
Release of assignment The Assignment is released in Digital Exam (DE) at exam start
Duration Written product to be submitted on specified date and time.
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Summer
Make-up exam/re-exam
Same examination form as the ordinary exam
The 1st retake is a 72-hour, maximum 10-pages home assignment. If the number of registered candidates for the make-up examination/re-take examination warrants that it may most appropriately be held as an oral examination, the programme office will inform the students that the make-up examination/re-take examination will be held as an oral examination instead.
Description of the exam procedure

The assessment consists of an assignment with several questions related to business intelligence. Students will be provided with a brief and accompanying datasets, and will be required to carry out data visualisation and analysis using R. No prior programming experience is necessary, as the fundamental operations of the software are taught and practised in lectures and workshops. The report should include evidence of data operations and analysis (such as screenshots and plots), together with the business insights derived from the analysis.
Course content, structure and pedagogical approach

Business intelligence refers to the technologies, applications and practices used to collect, integrate, analyse and present business data in support of decision-making. This course develops students’ analytical skills for business decision-making and evaluation, combining lectures and workshops with a strong emphasis on practical application. Students make extensive use of R and its widely adopted packages, including tidyverse, tidymodels and tidytext.
 

  • Preliminary assignment: A small assignment
  • Session 1: Introduction
  • Session 2: Understanding business data
  • Session 3: Descriptive analytics
  • Session 4: Efficient data manipulation in R
  • Session 5: Regression methods
  • Midway assignment: A small assignment
  • Session 6: Classification methods
  • Session 7: Data pre-processing, model training and evaluation
  • Session 8: Cluster analysis
  • Session 9: Text processing with R
  • Session 10: Sentiment analysis and topic modelling
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
  • Analysis
Description of the teaching methods
Teaching comprises face-to-face lectures and workshops for each session. Lectures introduce key concepts, theories and methodologies, while workshops provide hands-on, formative activities. Students are advised to bring their laptops to engage fully in the practical analytics exercises.
Feedback during the teaching period
Formative assessment and feedback will be embedded within session exercises. This interactive approach is designed to consolidate understanding and develop practical skills, ensuring that students are well prepared for the summative assessments.
Student workload
Preliminary assignment 20 hours
Classroom attendance 30 hours
Preparation 129 hours
Feedback activity 7 hours
Examination 20 hours
Further Information

6-week course.

 

Precourse activity: The course coordinator uploads precourse activity on Canvas at the end of May. It is expected that students participate as it will be included in the final exam, but the assignment is without independent assessment and grading.

 
Expected literature

Recommended textbooks:

  • Jay Gendron. Introduction to R for Business Intelligence. Packt Publishing, 2016
  • Julia Silge and David Robinson. Text Mining with R: A Tidy Approach. O'Reilly, 2016
Last updated on 03/11/2025