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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.