05-27, 14:30–15:00 (Europe/Vilnius), PyData Room
The abundance of behavioral data online and cutting-edge computational techniques seem to promise an easier way to explain and model complex human behavior. However, elegant math theories and multiplex models are largely disconnected from social science theories. There is a divide between “traditional” social scientists and data scientists in approach to data analysis.
I offer ways to address this divide in order to further advance computational social science and give examples of use of Python in social science research, from data collection to data analysis.
python, data science, best practices
Inga Popovaitė is a researcher at Kaunas University of Technology. She has a PhD in Sociology and a graduate certificate in Informatics from the University of Iowa. She uses computational social science methods, among others, to investigate group behavior in isolated, confined, and extreme environments. Her recent work has been published by Journal of Human Behavior in Extreme Environments and Acta Astronautica.