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2023/2024  KAN-CCMVI2071U  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 cand.merc. and GMA (CM)
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 22/11/2023

Relevant links

Learning objectives
By the end of this course students will be able to:
  • Explain the key concepts of business intelligence
  • Identify types of analytics used in business
  • Explain how data-driven decision-making impacts business
  • Identify types and formats of data
  • Effectively use R to process, summarize and visualize business data
  • Display a comprehensive understanding of a wide range of quantitative methods and machine learning techniques
  • Appropriately choose and appraise methods for specific business problems
  • Effectively use R to develop machine learning models for business intelligence
Course prerequisites
This is a course for graduate students in business subjects (e.g., information systems, marketing, operations research, management, business analytics). No mathematics and programming knowledge and skills are needed for entering this course. However, students with basic mathematical or analytical skills are preferred.
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


Assignment: data analytics report (100%)

  • The assessment is based on an assignment with several data science questions related to business intelligence.
  • Students will be given datasets and need to proceed data visualization and analysis using R.
  • No previous programming experience is needed, and the fundamental operations of both tools are taught and practiced in lectures and workshops.
  • Students need to provide evidence of their data operations and analysis (including screenshots and plots) as well as business insights from data analysis.
Course content, structure and pedagogical approach

Business intelligence refers to technologies, applications and practices for the collection, integration, analysis, and presentation of business data in order to support business decision making. Essentially, it is a collection of data-driven decision support models. This course teaches students analytical skills on data to support decision making and evaluation in business. It uses a combination of lectures and workshops. The course emphasizes the practical applications and makes extensive use of R for intelligent business analytics.


Preliminary assignment: A small assignment (with several questions)

Session 1 Introduction

Session 2 Understanding business data

Session 3 Descriptive analytics

Session 4 Efficient data manipulation in R

Session 5 Regression methods (for sales or price forecasting, etc.)

Session 6 Classification methods (for fraud detection, customer engagement analysis, consumer segmentation, etc.)

Feedback activity: A small assignment (with several questions)

Session 7 Data pre-processing, model training and evaluation

Session 8 Cluster analysis (for consumer segmentation, etc.)

Session 9 Text processing with R (e.g., string operations, regular expression)

Session 10 Sentiment analysis and topic modelling

Session 11 Course review and Q&A

Description of the teaching methods
The teaching methods involve face-to-face instruction in the classroom, and students are advised to bring their laptops for the purpose of conducting practical analytics exercises.
Feedback during the teaching period
Student survey feedback.
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.


Preliminary Assignment: The course coordinator uploads Preliminary Assignment 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 22/11/2023