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2019/2020  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 80
Study board
Study Board for MSc in Economics and Business Administration
Course coordinator
  • Bowei Chen - Department of International Economics, Governance and Business (EGB)
For academic questions related to the course, please contact instructor Bowei Chen at bc.acc@cbs.dk
Main academic disciplines
  • Management
  • Marketing
  • Economics
Teaching methods
  • Online teaching
Last updated on 16-04-2020

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 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, linear algebra and probability. No programming skills are needed.
Examination
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-point grading scale
Examiner(s) One internal examiner
Exam period Summer, Ordinary exam: Home Assignment: 23/24 June-24 July 2020. 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: 5–8 October 2020 – for all ISUP courses simultaneously
3rd attempt (2nd retake) exam: 72-hour home assignment: 23–26 November 2020 – 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, 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 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 and Microsoft Azure Machine Learning Studio
Class 2: Understanding Business Data Types and Structure 
Class 3: Summarizing and Presenting Business Data 
Class 4: Forecasting Sales Using Linear Regression
Class 5: Identifying Fraudulent Card Transactions Using
Logistic Regression
Class 6: Segmenting Consumers and Forecasting Sales
Using Artificial Neural Networks
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 
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 Final Assignment
 
Description of the teaching methods
This year all courses are taught digitally over the Internet. Instructors will apply a mixture of direct teaching through a live link (like Skype, Team, Zoom…) and indirect, where visual pre-recorded material is uploaded on Canvas. The instructor will inform participants about the precise format on Canvas.
Feedback during the teaching period
Student survey feedback.

Home Project Assignments/mini projects are based on a research question (problem formulation) formulated by the students individually. Approval deadline will be defined by the instructor. Hand-in of the problem formulation directly to the instructor by the 3rd teaching week.

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 March 2020.

 

 

 
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
 
Maxime Cohen. Big Data and Service Operations. Production and Operations Management, Vol. 27, No. 9, 2018, pp. 1709-1723

 

 

Additional relevant readings:

 

Roger Barga, Valentine Fontama, Wee Hyong Tok. Predictive Analytics with Microsoft Azure Machine Learning, Apress, 2015
 
Last updated on 16-04-2020