BACKGROUND
Python is a powerful and versatile programming language that offers valuable tools for working with qualitative data. Many qualitative researchers still overlook the potential of digital tools like Python for managing and analyzing qualitative data (see
Franken 2022 for discussion), while data science often focuses on large datasets.
In this training, we address this gap by introducing essential Python methods tailored to the needs of qualitative research.
WORKSHOP GOAL
By the end of this workshop, participants will have a foundational understanding of Python and its potential for qualitative data 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.
WORKSHOP CONTENT
- Overview of the relevance and applications of Python in qualitative research.
- 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 in Python.
TARGET AUDIENCE & PRIOR KNOWLEDGE
The workshop is designed for individuals with
little to no prior experience in Python who are interested in using it for qualitative data analysis in their research.
It is also the ideal preparation for the workshop “
From Audio to Text: Automated Transcriptions with Whisper”.
TECHNICAL REQUIREMENTS
ABOUT THE TRAINER
Annika Nolte and Nele Fuchs are data scientists for training and consulting at the DSC.
Annika holds a master’s degree in Environmental Sciences from the Technical University of Braunschweig (2019). She has over five years of experience in scientific programming, specializing in data management and processing, hydroinformatics, geospatial analysis, and AI for environmental modeling. In training and consulting, Annika draws on her research background and broad interdisciplinary expertise in Earth system sciences.
Nele Fuchs studied Philosophy, Material Culture: Textile (CvO University of Oldenburg), and Transcultural Studies (University of Bremen). As a data scientist in the humanities, she supports researchers in the areas of digital humanities, data science methods for qualitative research and FAIR-compliant qualitative data management, using her expertise in handling sensitive qualitative data.