PRACTICAL ASPECTS OF DATA SCIENCE
Applying data science in a business environemnt includes many differnt aspects. Analysing data and finding a promising prediction model is often only a small fraction of the daily work of a data scientist.
In this course, we look at different challenges that a data scientist will face in industry. With the help of a running example, the students learn to understand what problems are related to the work of Data Scientist and which best practices can be applied to solve them.
– Mastering model training
– How to properly evaluate models
– Handling missing input features
– Probability calibration
– Monitoring of your ML system
– Interpretation of models
– Tracability of code and technical debt