Compulsory module (3 ECTS)
Fundamentals of Data Science (3 ECTS)
Content:
- Introduction to data science
- Infrastructure for data scientists
- Basics of data analysis
- Simple text analysis
- Communicating results
- Critical aspects of data analysis
Required elective modules (9 ECTS)
Programming
- Python for Data Science (6 ECTS)
- R Bootcamp (3 ECTS)
Basic methods
- Linear Algebra 1 (3 ECTS)
- Data Quality (3 ECTS)
Domain experience
- Customer Data Analytics (3 ECTS)
- Data Analytics for Energy Systems and IoT (3 ECTS)
- Fraud Detection (3 ECTS)
- Geospatial Data Analysis for Smart Communities (3 ECTS)
- Open (Government) Data with Tableau (3 ECTS)
- Sport Data Analytics (3 ECTS)
- Time Series Analysis in Finance (3 ECTS)
- Sustainability Analytics (3 ECTS)
- Natural Experiments Using R (requires R Bootcamp) (3 ECTS)
Human centered topics
- Ethical Issues of Big Data (3 ECTS)
- Legal Issues of Big Data (3 ECTS)
- Human Centered Design (3 ECTS)
- Data Visualisation and Narration (3 ECTS)
- Hands-on Visualisation for Data Science (3 ECTS)
- GenAI (requires Python for Data Science) (3 ECTS)
On request
- Machine Learning I (3 ECTS)
Click or tap here to consult the module descriptions for the required elective modules.
General information
The modules are taught in English and delivered on Thursdays or Fridays in each semester. You may attend them concurrently or consecutively to your master's programme.
After completing the master’s programme or—if taken consecutively—the Minor in Data Science, you will earn an additional diploma for your advanced skills in Data Science.
Applications for admission to the Minor in Data Science must be filed with the Head of Programme or Head of Major in the regular master's programme.
To be able to take an active role in the English-taught classes, participants must have English-language skills at CEFR level C1 or higher (B1 may be accepted with conditions). The module assessed assignments may be completed in German or English.
Voluntary introductory courses in R and Python are available to prepare for the Minor in Data Science. Once accepted to the Minor in Data Science, students may access the relevant course materials and instructions here.