PRACTICAL ASPECTS OF DATA SCIENCE

LEARNING OUTCOME

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.

CONTENT

– 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