Information

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.

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.

Documents

I. Lecture

Version: April 28, 2019

Download lessons:

Lecture slides
Datasets

Download notebooks (all):

Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5

II. Exercises

Version: April 28, 2019

Download exercises (all):

Exercises A
Exercises B
Exercises C

Download solutions (all):

Solutions A
Solutions B
Solutions C

III. Self-tests

Version: May 2, 2019

Test yourself:

Chapter 1
Chapter 2
Chapter 3
Chapter 4
Chapter 5

Cloud

The GWDG provides a Jupyter-Hub for members of the University of Goettingen and all who have access to the GWDG. This is a Jupyter notebook environment that runs in the cloud. Course participants can import all notebooks directly into their private workspace and start programming.

Errata

We are actively revising all materials. If you find a mistake, please write us a short hint.