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2018/2019  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
Max. participants 60
Study board
Study Board for MSc in Economics and Business Administration
Course coordinator
  • Course instructor - Dr. Bowei Chen, Assistant Professor, Adam Smith Business School, University of Glasgow, UK, Email: bowei.chen@glasgow.ac.uk
    Sven Bislev - Department of Management, Society and Communication (MSC)
In case of any academic questions related to the course, please contact the course instructor or ISUP academic director, Sven Bislev at sb.msc@cbs.dk
Main academic disciplines
  • Management
  • Marketing
  • Supply chain management and logistics
Teaching methods
  • Face-to-face teaching
Last updated on 05/12/2018

Relevant links

Learning objectives
To achieve the grade 12, students should meet the following learning objectives with no or only minor mistakes or errors:
  • Explain the key concepts of business intelligence
  • Identify types of analytical models used in business
  • Explain how data-driven decision making impacts business
  • Identify types of data
  • Effectively use Excel and Microsoft Azure Machine Learning Studio 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 and technology for specific business problems
Course prerequisites
This is an introductory course for MSc students in business studies. It is self-contained and fundamental mathematics will be reviewed. Students are expected to have basic mathematics knowledge such as calculus and linear algebra. No programming skills is needed.
Business Intelligence:
Exam ECTS 7.5
Examination form Home assignment - written product
Individual or group exam Individual exam
Size of written product Max. 15 pages
Assignment type Project
Duration Written product to be submitted on specified date and time.
Grading scale 7-step scale
Examiner(s) One internal examiner
Exam period Summer, Ordinary exam: Home Assignment: 25/26 June - 29 July 2019. Please note that exam will start on the first teaching day and will run in parallel with the course.
Retake exam: Home Assignment: 72-hour home assignment: 8-11 October 2019 – for all ISUP courses simultaneously
3rd attempt (2nd retake) exam: 72-hour home assignment: 25-28 November 2019 – for all ISUP courses simultaneously

Exam schedules available on https:/​/​www.cbs.dk/​uddannelse/​international-summer-university-programme-isup/​courses-and-exams
Make-up exam/re-exam
Same examination form as the ordinary exam
Retake exam: 72-hour home project assignment, max. 10 pages, new exam question
Exam form for 3rd attempt (2nd retake): 72-hour home project assignment, max. 10 pages, new exam question
Course content and structure

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 empirical 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 Excel and Microsoft Azure Machine Learning Studio for intelligent business analytics.


Each class will be held in the classroom. It is combined with lecture (theory) and workshop (practice). Students will need to bring their own laptops to the classroom (connected to Wifi) or the teaching can be delivered in the computer lab.  
Preliminary Assignment: A couple of questions related to Business Intelligence

Class 1: Introduction to Business Intelligence

Class 2: Understanding Business Data Types and Structure 

Class 3: Summarizing and Presenting Business Data 

Class 4: Forecasting Sales Using Linear Regression

Class 5: Forecasting Demand Using Artificial Neural Networks

Class 6: Identifying Fraudulent Card Transactions Using Logistic Regression Feedback Activity: a small assignment (with several questions) 

Class 7: Predicting Customers’ Feedback Ratings Using Tree-Based Models

Class 8: Segmenting Consumers Using Naive Bayes and K-Nearest Neighborhood Methods

Class 9: Recommending Products to Customers Using Collaborative Filtering Techniques

Class 10: Segmenting Consumers Using Cluster Analysis

Class 11: Review Session and Q&A for the Assignment

Description of the teaching methods
Each class will be held in the classroom. It is combined with lecture (theory) and workshop (practice). Students will need to bring their own laptops to the classroom (connected to Wifi) or the teaching can be delivered in the computer lab.
Feedback during the teaching period
Students will get a mini/small assignment based on the first 6 classes, helping them to understand how they are doing academically, and what to expect from the final assignment.

All Home Project Assignments/mini projects are based upon a research question (problem formulation) formulated by the students individually, and must be handed in to the course instructor for his/her approval no later than 11 July 2019. The instructor must approve the research question (problem formulation) no later than 16 July 2019. The approval is a feedback to the student about the instructor's assessment of the problem's relevance and the possibilities of producing a good report.
Student workload
Preliminary assignment 20 hours
Classroom attendance 33 hours
Preparation 126 hours
Feedback activity 7 hours
Examination 20 hours
Further Information

Preliminary Assignment: To help students get maximum value from ISUP courses, instructors provide a reading or a small number of readings or video clips to be read or viewed before the start of classes with a related task scheduled for class 1 in order to 'jump-start' the learning process.


Course timetable is available on https://www.cbs.dk/uddannelse/international-summer-university-programme-isup/courses-and-exams


We reserve the right to cancel the course if we do not get enough applications. This will be communicated on https://www.cbs.dk/uddannelse/international-summer-university-programme-isup/courses-and-exams end February 2019 at the latest.




Expected literature

Mandatory readings:


Anil Maheshwari. Business Intelligence and Data Mining. Business Expert Press, 2015, Chapters 1-8 


Hsinchun Chen, Roger Chiang and Veda Storey. Business Intelligence and Analytics: From Big Data to Big Impact. MIS Quarterly, Vol. 36, No. 4, 2012, pp. 1165-1188 


Additional relevant readings:


Maxime Cohen. Big Data and Service Operations. Production and Operations Management, Vol. 27, No. 9, 2018, pp. 1709-1723 Roger Barga, Valentine Fontama, Wee Hyong Tok. Predictive Analytics with Microsoft Azure Machine Learning, Apress, 2015 

Last updated on 05/12/2018