DS400 Data Science Project Management

Lecturer: David Gremminger, M.Sc.
Course description:


Language: English
Recommended for this semester or higher: 1
ECTS-Credits: 9
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:

DS404B Big Data Computing


Previous knowledge in programming is required (preferrably with R). Basic knowledge about R and statistics.

Course Type: Lecture (2 weekly lecture hours)

Will be offered via asynchronous lecture videos weekly + bi-weekly Q&A sessions.
Beginning of the first session: Tuesday, October 19, 2021 from 10am c.t. - 12 noon (room HS 14 Neue Aula)
+++As of now, we plan to have ~bi-weekly Q&A sessions in person. While we hope to make this happen as planned, we are ready to move online, if necessary. In this case, a corresponding Zoom-Link would be communicated in ILIAS. The dates for the Q&A sessions are available in ILIAS.+++


Limited to 45 participants. Registration open for all (no first-come, first-served). Registration will open on September 1, 2021 on Alma - end of registration time: Sunday, October 10, 2021 (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. Previous knowledge in programming is required (preferrably with R).

Downloads: Ilias
Method of Assessment:

Written exam (90 Minutes), or oral examination, or assignments or presentation or online assessment (90 Minutes)
Examination Timetable will be available on the website of the examination office.


The course deals with the workflow of empirical analyses, applying all steps from data collection through data management to data output using the statistical software R. The focus is on dealing with large data sets and implementation of all data preparation and analysis steps in code to ensure replicability.


Through the tutorial, students will be able to reflect on the concepts presented in theory, and to apply these concepts to advanced research problems.


Grolemund, G. & Wickham, H. (2017): R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. O’Reilly Media.