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2020/2021  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, Goverment and Business (EGB)
For academic questions related to the course, please contact instructor Bowei Chen at bc.acc@cbs.dk or Bowei.Chen@glasgow.ac.uk
Main academic disciplines
  • Management
  • Marketing
  • Supply chain management and logistics
Teaching methods
  • Online teaching
Last updated on 04-03-2021

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 Microsoft 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 subjects. 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: 22 June-30 July 2021. Please note that exam will start on the first teaching day and will run in parallel with the course.
Retake exam: 72-hour home assignment: 27 – 30 September 2021 – for all ISUP courses simultaneously.
3rd attempt (2nd retake) exam: 72-hour home assignment: 22 – 25 November 2021 – for all ISUP courses simultaneously
Exam schedules are/will be 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 assignment, max. 10 pages, new exam question.
Exam form for 3rd attempt (2nd retake): 72-hour home 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 emphasizes the practical applications and makes extensive use of Microsoft Excel and Microsoft Azure Machine Learning Studio for intelligent business analytics. Each class is combined with lecture (theory) and workshop (practice). Students need to have their computers with Internet access. 

 

Preliminary assignment: A small assignment (with several questions) 

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: Data preprocessing, model training and evaluation

Feedback activity: A small assignment (with several questions) 

Class 7: Segmenting consumers and forecasting sales using artificial neural networks

Class 8: Predicting customers’ feedback ratings using tree-based models

Class 9: Recommender systems

Class 10: Segmenting consumers using cluster analysis

Class 11: Course review and Q&A for the assignment

 

Description of the teaching methods
This year all courses are taught digitally over the Internet. Instructors will apply direct/live teaching through a link (like Skype, Team, Zoom). In some courses, pre-recorded material will also be used.
Feedback during the teaching period
Student survey feedback.
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/will be 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 in in March 2021.

Expected literature

Mandatory readings:

  • Anil Maheshwari. Business Intelligence and Data Mining. Business Expert Press, 2015, Chapters 1-8
  • Jeff Barnes. Azure Machine Learning Microsoft Azure Essentials, Microsoft Press, 2015
  • 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 04-03-2021