In recent years, Python has established itself alongside R at the forefront of numerical programming languages. Very similar to the programming with MATLAB, mathematical-statistical representations from technical literature, such as econometric textbooks, can be implemented compactly and easily in the programming language Python and its scientific extensions. Following a concise introduction to the general-purpose language framework, the students learn how to design, implement and exchange their own data analysis projects in an object-oriented way:

1. Introduction to Python and object orientation.
2. Numerical programming – compared to MATLAB and R.
3. Data formats, handling, exports and imports – file and web.
4. Visual illustrations and presentation of scientific results.
5. Statistical analysis with further applications in economics.

Extension for a time series econometrics course (stable version to be published):

1. Basics and univariate time series modeling.
2. Multivariate time series modeling and testing.

The participants get familiar with Python’s way of thinking and learn how to solve (scientific) programming problems with a state-of-the-art tool.