DSC-2026-02 | Introduction to Python for Researchers

Why is the topic important?

Python is a powerful and versatile programming language that can be used for a wide range of research tasks involving both qualitative and quantitative data.

Researchers from a variety of disciplines, including environmental science, digital humanities, engineering, and bioinformatics, will find this workshop's accessible introduction to essential Python methods and tools invaluable. Throughout, the workshop emphasizes reproducible research workflows and highlights Python’s strengths for automating data preparation, supporting transparent analysis, and working flexibly with both text-based and numeric data.

Workshop Goal

By the end of this workshop, participants will have an initial understanding of Python and an impression of its potential for qualitative or quantitative data processing and analysis. They will gain hands-on experience with basic programming concepts, enabling them to write simple scripts and begin applying Python to their own research. The first day covers the fundamentals of Python. On the second day, we apply these basics to a range of qualitative and quantitative research tasks, with participants choosing a primary focus area. For qualitative research, we will work through examples of how Python can support computer-assisted approaches (see Franken 2022). While advanced natural language processing will not be part of the workshop, participants will leave with practical skills and ideas for working with their own qualitative data. For quantitative research, we focus on foundational analysis, including importing and cleaning tabular data, simple descriptive statistics, basic visualizations, and a first step into modeling (e.g., linear regression, trend analysis).

Workshop Content

DAY 1: 

• Python overview: history and background, why it’s widely used including in research, and the basics of installation, tools, and environments.
• Key concepts including variables, data types, and basic syntax.
• Small, guided exercises to reinforce learning and build confidence.
• Introduction to reading, processing, and managing text data and numerical data in Python.

DAY 2:

• Recap of Python basics (Day 1).
• Focus area qualitative research/digital humanities: Loading and organizing text files in Python, including managing basic metadata; working with structured data (parsing HTML for web scraping and handling tabular data); basics of Natural Language Processing (NLP), including text preprocessing and sample use cases.
• Focus area quantitative research: Loading and pre-processing numerical (tabular) data; datasets with simple descriptive statistics and grouped summaries; creating basic visualizations for exploration and communication (e.g., histograms, scatterplots, time series plots; maps); an introduction to simple analyses (e.g., correlation, linear regression, or trend analysis).
   

Target Audience & Prior Knowledge

The workshop is designed for researchers with little to no prior experience in Python and/or another programming language who are interested in using it for data analysis in their research. It is aimed at researchers who do not have a background in computer science and are curious to enter the world of programming.

Technical Requirements

• Own laptop and connection to the Wifi
  (e.g. via eduroam -> /en/zfn/wifi/overview-wifi).
• Please make sure you have access to the Jupyter4NFDI.

About the Trainers

Annika Nolte and Nele Fuchs are data scientists for training and consulting at the DSC.