Data Science in Agricultural Computer Science - Data Science in der Agrarinformatik
Responsible: M.Sc. M. von Bloh
Lecturer: M.Sc. M. von Bloh
Language: German
Level: Master
Credits: 5 ECTS
Teaching method: Lecture + Exercise
The module consists of a lecture and an exercise in which agricultural scientists are familiarised with the topic of automated data analysis.
Among others, the following topics are covered:
- Basics of programming in Python + Linux
- Data preparation & analysis (cleaning data, normalising data, feature engineering, continuous vs. discrete data)
- Data visualisation (distributions, time series)
- Basics of statistical learning (supervised and unsupervised learning, regression/classification models, clustering, evaluation of statistical models)
In addition, there is a semester project that includes a programming performance in the Python programming language. Here the students learn 'hands-on':
- Automated evaluation of an agricultural dataset
- Visualise the data and extract the most important features
- Create their own data pipeline
- Develop their own predictive model