While there are many powerful programming languages that one could use for solving data science problems, people forget that one of the most powerful and simplest tools to use is right under their noses. And that is UNIX. The name may generate images of old-time hackers hacking away on monochrome terminals. Or, it may hearken the idea of UNIX as a mainframe system, taking up lots of space in some warehouse. But, while UNIX is indeed one of the oldest computing platforms, it is quite sophisticated and supremely capable of handling almost any kind of computational and data problem. In fact, in many respects, UNIX is leaps and bounds ahead of other operating systems; it can do things of which others can only dream!

So far, our work on data science problems has primarily involved applying statistical techniques to analyze the data and derive some conclusions or insights. But there are times when it is not as simple as that. Sometimes we want to learn something from that data and use that learning or knowledge to solve not only the current problem but also future data problems. We might want to look at shopping data at a grocery chain, combined with farming and poultry data, and learn how supply and demand are related. This would enable us to make recommendations for investments in both the grocery store and the food industries.

Under which conditions will a public authority intervene in private governance such as certification and eco-labeling schemes for sustainably produced goods? This chapter introduces this research question by presenting the empirical puzzle the book addresses: Why has the European Union (EU) intervened in private governance that deals with organic agriculture and biofuels, but has not intervened in private governance dealing with fair trade and fisheries? The chapter distinguishes between a public authority intervening with standards regulation that involves creating a public definition of sustainable production, and with procedural regulation that addresses the way private governance schemes are organized. The argument the book develops is that whether a public authority intervenes with standards and/or procedural regulation depends on the interplay of two variables: the domestic benefits of product differentiation by a public authority and the fragmentation of the private governance market. The chapter situates the book in the current state of the literature on the interactions between public and private governance and explains the research design and research contributions.

In the previous chapter, we saw how to learn from data when the labels or true values associated with them are available. In other words, we knew what was right or wrong and we used that information to build a regression or classification model that could then make predictions for new data. Such a process fell under supervised learning. Now, we will consider the other big area of machine learning where we do not know true labels or values with the given data, and yet we will want to learn the underlying structure of that data and be able to explain it. This is called unsupervised learning.

This chapter presents a series of empirical analyses to test nationalization's primary effects on revenues and secondary effects on political survival. It begins by assessing the claim that nationalization will foster greater government take of resource revenues compared to maintaining operations by private firms. It then examines whether this corresponds to a higher probability of leadership survival: if nationalization increases state capture of resource revenues, then it should be the case that leaders use this wealth to consolidate power and prevent ouster. Beyond the survival of political leaders, it should also be true that political regimes in general will be stronger if resources are nationalized. These hypotheses are tested using the complete cross-national NOC dataset in conjunction with existing data on government revenues, the breakdown of regimes, and leadership survival. The empirics support the theory: nationalization increases state capture of resource revenues and increases the likelihood of survival of leaders and their political regimes. The results suggest that nationalizing operations explains why resource-rich leaders survive in some countries but not others.