Marija Mitrović Dankulov

Associate Research Professor - Institute of Physics Belgrade

Marija Mitrović Dankulov is the Head of Innovation Center at the IPB and an Associate Research Professor at IPB.

She completed her Ph.D. in statistical physics at the Faculty of Physics, University of Belgrade in 2012. After her Ph.D. studies at the Department of Theoretical Physics, Institute Jožef Stefan, Slovenia, she undertook postdoctoral work at Department of Biomedical Engineering and Computational Science, School of Science Aalto University, Finland.

She has extensive knowledge and experience in theoretical and computational physics. Her primary research interest is statistical physics of complex systems, with the emphasis on physics of social behaviour and complex networks theory.

As a Head of Innovation Center she is involved in technology development and commercialisation, IP protection and management, R&D and commercialisation project management.

PyCon Balkan 2019 Talks

Python and computational social science

The idea that social phenomena should also follow precise quantitative such as one existing in physics is more than two centuries old. We still lack social science equivalent to Newton's laws. One of the reasons for this is the deficiency of large detailed data about human social behaviour. The rapid development of information and communication technologies has changed this. Social data at a large scale is nowadays available over the internet.

We need tools that allow us to collect, store and analyze these massive amounts of data. Scientists from different fields including mathematics, statistics, computer science, physics, sociology and economics adapt old and develop new methods to search for statistical laws of social phenomena in this data. They all contribute to the development of new field commonly known as 'computational social science'. Python has an essential role in the development of this new interdisciplinary field. It is comprehensive, flexible and easy to learn and use. Its extensive standard library and collection of modules, as well as a large community of developers, makes it sufficient for doing research in computational social science. I will demonstrate its comprehensiveness and sufficiency on examples from my research. I will discuss in more details the modules used for analyzing complex networks and their visualization.

keynote

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