DS405 Machine Learning Applications in Business and Economics / Online

Lecturer: Prof. Dr. Stefan Mayer
Course description:


Language: English
Recommended for this semester or higher: 1
ECTS-Credits: 6
Course can be taken as part of following programs/modules:

Economics and Finance
European Management
General Management
International Business
International Economics
Management and Economics

Data Science in Business and Economics

Prerequisite for:



Basic knowledge about R and statistics.

Course Type: Lecture (2 weekly lecture hours)

Thursdays from 10am c.t. - 2pm (Beginning of the first lecture: January 7, 2021) asynchronous with several Q&A sessions


Limited to 45 participants. Registration open for all (no first-come, first-served): Registration will open on Monday, November 2, 2020 on ILIAS - end of registration time: Sunday, November 22, 2020 (23:55pm.). If the number of applications (limited to 45 participants) exceeds the number of places available, we unfortunately will not be able to accept all of the applicants. In this case, a random selection will be made from all the applications received. Preferred access for master students from the Data Science in Business and Economics program.

Downloads: ILIAS
Method of Assessment: Written exam (90 Minutes), or oral examination, or assignments or presentation or online assessment (90 Minutes)
Exam dates are available on the website of the Examinations Office.

Machine Learning Methods have strongly grown in popularity over recevent years, and they are now frequently used alongside or even instead of traditional statistical methods. This module will therefore consider selected Machine Learning methods and study their use and applicability across different fields of business and economics. The focus of this module will be on the application of these methods across different fields, and less on the theoretical background.


Students can reflect on the proper use of Machine Learning techniques in different subfields of business and economics, they can discuss and evaluate the benefits of different Machine Leaning methods vis-à-vis traditional statistcal methods. Students know how to implement these Machine Learning methods in appropriate statistical software (e.g., R), and how to interpret the results.


Lantz, B. (2015). Machine Learning with R (2nd ed.). Packt Publishing Ltd.
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning: With Applications in R. Springer.