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2017/2018  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
Start time of the course 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)
Kontaktinformation: https:/​/​e-campus.dk/​studium/​kontakt eller Contact information: https:/​/​e-campus.dk/​studium/​kontakt
Main academic disciplines
  • Finance
  • Marketing
  • Economics
Last updated on 22-02-2017

Relevant links
Learning objectives
To achieve the grade 12, students should meet the following learning objectives with no or only minor mistakes or errors: 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
Examination
Data Science: Data Driven Decision Making:
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 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
Make-up exam/re-exam
Same examination form as the ordinary exam
* if the student fails the ordinary exam the course coordinator chooses whether the student will have to hand in a revised product for the re- take or a new project.
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
Feedback during the teaching period
Office hours and workshops
Student workload
Preparation 101 hours
Classes 33 hours
Exam 72 hours
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. Hastie, R. Tibshirani and J. Friedman (2009), The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd Edition. Springer.

 

F. Provost and T. Fawcett (2013), Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking. O’Reilly. 
Last updated on 22-02-2017