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2024/2025  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 First Quarter
Timetable Course schedule will be posted at calendar.cbs.dk
Max. participants 200
Study board
Study Board for cand.merc. and GMA (CM)
Course coordinator
  • Dolores Romero Morales - Department of Economics (ECON)
Main academic disciplines
  • Finance
  • Marketing
  • Economics
Teaching methods
  • Face-to-face teaching
Last updated on 01-02-2024

Relevant links
Learning objectives
  • Demonstrate awareness of how Data Driven Decision Making helps to establish competitive advantages
  • Demonstrate knowledge of Supervised Learning theory in Decision Making, such as in Credit Scoring
  • Demonstrate knowledge of Unsupervised Learning theory in Decision Making, such as in Customer Segmentation
  • Be confident users of package computer programs that are widely used in industry for Supervised and Unsupervised Learning
  • Demonstrate critically assessment of different visualization tools for reporting final results
Course prerequisites
The students should have a thorough knowledge of :
• descriptive statistics,
• basic linear regression models,
• basic algebra
Otherwise, it will be rather challenging to follow the course.
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
Release of assignment The Assignment is released in Digital Exam (DE) at exam start
Duration 2 weeks to prepare
Grading scale 7-point grading scale
Examiner(s) One internal examiner
Exam period Autumn
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.
Description of the exam procedure

Two week home assignment will be circulated to the students on the last day of the course 
Course content, structure and pedagogical approach

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. You will be exposed to the mathematical optimization models behind many of these tools, and the advantages that this mathematical modelling bring. The course uses computer software to illustrate how to apply the methodologies introduced. The course is multidisciplinary in nature and links to areas such as accounting, economics, finance, marketing, and operations management.

 

The course’s development of personal competences:

 

During the course, and through a hands-on approach supported by Supervised and Unsupervised Learning theory, students will develop quantitative as well as mathematical modelling skills needed for Data Driven Decision Making. In addition, students will learn to appreciate the importance of using the right visualization tool to report final results.
Description of the teaching methods
Lectures, Demos, Computer Workshops
Feedback during the teaching period
The students will receive feedback at different points in time, including during the hands-on PC Workshop sessions, during group Q&A sessions that will be scheduled throughout the duration of the course, as well as during the office hours.
Student workload
Preparation 104 hours
Classes 30 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

 

G. James, D. Witten, T. Hastie and R. Tibshirani (2021), An Introduction to Statistical Learning: with Applications in R. 2nd Edition. Springer.

 
Last updated on 01-02-2024