DS400 Data Science Project Management
| Lecturer: | Prof. Dr. Stefan Mayer |
| Course code: | DS400 |
| Language: | English |
| Recommended for this semester or higher: | 1 |
| ECTS-credits: | 9 |
| Course can be taken as part of following programs/modules: | See alma |
| Prerequisites: | Previous knowledge in programming is required (either R or Python, ideally basic knowledge in both), as well as a basic understanding of statistics. Ensure to review the provided R / Python refresher slides and videos before the first lecture. |
| Limited attendance: | 28 |
| Course type: | Lecture |
| Date: | Tuesdays, 2 pm - 6 pm c.t., PC Lab 008 (Nauklerstr. 47) First lecture on October 14, 2025 |
| Registration: | By October 19 via alma
If the number of applications exceeds the number of places available, a random selection will be made from all the applications received. |
| Method of assessment: | Written exam (90 Minutes), Assignments |
| Content: | The course deals with the workflow of empirical analyses, applying all steps from data collection through data management to data output. The focus is on dealing with large data sets and implementation of all data preparation and analysis steps in code to ensure replicability. The course will be taught “bilingual”: all procedures and analyses are explained both in R and Python, and students should be proficient in both languages by the end of the course. |
| Objectives: | Through the tutorial, students will be able to reflect on the concepts presented in theory, and to apply these concepts to advanced research problems. |
| Literature: | Grolemund, G. & Wickham, H. (2017): R for Data Science: Import, Tidy, Transform, Visualize, and Model Data. O’Reilly Media. VanderPlas, J. (2016). Python Data Science Handbook. O’Reilly Media. jakevdp.github.io/PythonDataScienceHandbook/ |
| Downloads: | --- |
| ILIAS: | Click here |