  This paper presents a three-component work. The first component sets the
overall theoretical context which lies in the argument that the increasing
complexity of the world has made it more difficult for International Relations
(IR) to succeed both in theory and practice. The era of information and the
events of the 21st century have moved IR theory and practice away from real
policy making (Walt, 2016) and have made it entrenched in opinions and
political theories difficult to prove. At the same time, the rise of the
"Fourth Paradigm - Data Intensive Scientific Discovery" (Hey et al., 2009) and
the strengthening of data science offer an alternative: "Computational
International Relations" (Unver, 2018). The use of traditional and contemporary
data-centered tools can help to update the field of IR by making it more
relevant to reality (Koutsoupias, Mikelis, 2020). The "wedding" between Data
Science and IR is no panacea though. Changes are required both in perceptions
and practices. Above all, for Data Science to enter IR, the relevant data must
exist. This is where the second component comes into play. I mine the CIA World
Factbook which provides cross-domain data covering all countries of the world.
Then, I execute various data preprocessing tasks peaking in simple machine
learning which imputes missing values providing with a more complete dataset.
Lastly, the third component presents various projects making use of the
produced dataset in order to illustrate the relevance of Data Science to IR
through practical examples. Then, ideas regarding the future development of
this project are discussed in order to optimize it and ensure continuity.
Overall, I hope to contribute to the "fourth paradigm" discussion in IR by
providing practical examples while providing at the same time the fuel for
future research.
