Introduction to Python for Social Scientists

Key Facts

When: 1st and 2nd December 2022, two day workshop
Where: online
Aiming at: Researchers in social science without prior knowledge of python
Aim: Introduction to programming with Python in Social Science
Registration: via LMS
For questions please contact Cora Molloy

The Workshop in Brief

Python is a general-purpose programming language that is useful for writing scripts to work effectively and reproducibly with data. Python is becoming increasingly popular among social and data scientists as a powerful tool to collect, handle and analyze large amounts of data, including various kinds of digital data collected from the web that can be analyzed with the help of machine learning techniques.

This workshop teaches data cleaning, management, analysis and visualization. There are no prerequisites, and the materials assume no prior knowledge about the tools. A single dataset is used throughout the workshop to model the data management and analysis workflow that a researcher would use.


This workshop addresses researchers at Max Planck Institutes without any prior knowledge of Python who would like to get a first grip on this powerful tool for data analysis. Please note that registration (through LMS) is binding.

What to Expect?

This workshop is an introduction to Python designed for participants with no prior programming experience. As you will learn this hands-on, you need software installed before. There might be an organisational meeting before the actual course. The lessons start with some basic information about Python syntax and the Jupyter notebook interface. The subsequent units deal with how to import CSV files, how to use the pandas package to work with data frames, how to calculate summary information from a data frame, and they provide a brief introduction to plotting.

More details on the contents can be found here:

Who are “The Carpentries”?

The Carpentries is a non-profit organisation that builds up a global community of volunteers teaching foundational computational and data science skills to researchers in academia, industry and government.