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2014/2015  KAN-CCMVV1402U  Data Science: Data Driven Decision Making

English Title
Data Science: Data Driven Decision Making

Course information

Language English
Course ECTS 7.5 ECTS
Type Elective
Level Full Degree Master
Duration One Quarter
Course period Third Quarter
Timetable Course schedule will be posted at calendar.cbs.dk
Study board
Study Board for MSc in Economics and Business Administration
Course coordinator
  • Dolores Romero Morales - Department of Economics (ECON)
Administrator: Tatjana Mastilo- tma.eco@cbs.dk
Main academic disciplines
  • Finance
  • Marketing
  • Economics, macro economics and managerial economics
Last updated on 10-09-2014
Learning objectives
After completing this course, the students should be able to:
  • Appreciate the competitive advantage of Data Driven Decision Making
  • Understand the use of Supervised Learning in Decision Making, such as in Credit Scoring
  • Understand the use of Unsupervised Learning in Decision Making, such as in Customer Segmentation
  • Use package computer programs, widely used in industry, for Supervised and Unsupervised Learning
  • Critically assess different visualization tools for reporting final results
Individual home assignment:
Exam ECTS 7,5
Examination form Home assignment - written product
Individual or group exam Individual
Size of written product Max. 10 pages
Assignment type Written assignment
Duration Written product to be submitted on specified date and time.
Grading scale 7-step scale
Examiner(s) One internal examiner
Exam period Spring Term and April
Make-up exam/re-exam
Same examination form as the ordinary exam
Course content and structure

In the current competitive environment, it is crucial to extract value from business data. In Data Science, rational business decisions are made after harnessing different sources of data. Typical examples are credit scoring, bankruptcy prediction, fraud detection, customer loyalty, recommender systems, and revenue management. This course aims to enhance your ability to apply Data Mining and Visualization tools for harnessing data. It is a practical course, which uses computer software to illustrate how to apply the methodologies introduced. The course is multidisciplinary with links to accounting, economics, finance, marketing and operations management.


During the course, and through a practical approach, students will develop quantitative skills necessary for Data Driven Decision Making, as well appreciate the importance of using the right visualization tool to report final results.

Teaching methods
Lectures, Demos, Computer Workshops
Expected literature

B. Baesens (2014), Analytics in a Big Data World: The Essential Guide to Data Science and its Applications. Wiley and SAS Business Series.


T.H. Davenport and D.J. Patil (2012), Data Scientist: The Sexiest Job of the 21st Century. Harvard Business Review 90(10): 70–76.

Last updated on 10-09-2014