This chapter provides some perspectives on decision-making and the role that it should play in your life to be most useful. Specific topics concern the misnomer of “objective decision-making, ” the inappropriateness of trying to optimize your decisions, the appropriate amount of time you should spend making decisions and how that time should be allocated, the importance of avoiding poor life-limiting decisions, the implications of your decisions on an untimely death, and how being spontaneous and enjoying your life fits in with your decision-making. There is also a summary of the types of personal nudges that you can create and use to guide you to make better decisions.

The book begins with three paradoxes of Libya, Venezuela, and Congo that juxtapose the profound importance of nationalization in the global natural resource economy with its economic risks and potential costs. The chapter then previews the answer to the puzzle of operational nationalization: when faced with the choice of nationalization, weak rulers discount the long-run costs of state intervention to seize its short-term gains; by contrast, strong leaders maintain the status quo of privately run operations to ensure long-term gains from private production. Next, the chapter illustrates the relevance of nationalization in a variety of research contexts: the effects of state intervention in the market; the roles of domestic leadership and international conditions in institutional choice; the significance of this choice during state formation; and the logic of the predatory state. After briefly introducing the theory of how, why, and when different operational nationalization pathways matter for politics, the chapter concludes by outlining how the remaining chapters of the book explain and test the operational nationalization theory.

There are many tools and techniques that a data scientist is expected to know or acquire as problems arise. Often, it is hard to separate tools and techniques. One whole section of this book (four chapters) is dedicated to teaching how to use various tools, and, as we learn about them, we also pick up and practice some essential techniques. This happens for two reasons. The first one is already mentioned here – it is hard to separate tools from techniques. Regarding the second reason – since our main purpose is not necessarily to master any programming tools, we will learn about programming languages and platforms in the context of solving data problems.

So far in this book we have taken one topic or tool at a time and looked at how we could tackle a given data problem. Now, it is time to start bringing them together to develop a deeper understanding of the nature of data problems and methods, as well as extend our reach and skillset to address new problems that may emerge. There is, of course, no way we could cover all that you would encounter in real life, but we can certainly try to go through a few examples to see where you could take your data science skills.

This chapter describes the operational nationalization dataset in detail. The chapter begins by defining state-owned enterprises (SOEs) with de facto control of operations as a measure of operational nationalization. After describing how operational nationalization is measured, the chapter explains the coding and construction of the 187-country, 116-year dataset of national oil companies (NOCs) based on primary and secondary sources of each country’s petroleum history. Only 70 of these countries are major producers, but for completeness the full sample includes all sovereign countries with populations greater than 200,000 in 2000. This chapter includes several brief examples of NOC varieties, cases of NOC reforms and privatizations over time, as well as varieties of nationalization in nonoil sectors like copper, coal, zinc, cobalt, and lithium. The chapter also discusses how the database compares with existing nationalization datasets.

The chapter explains how and why the EU has intervened with both standards and procedural regulations in the case of organic agriculture, first in 1991 and again in 2007. The chapter begins with analyzing the development of private organic agriculture governance since the late-1960s. It shows how attempts at private governance harmonization, the expectation of EU intervention providing new productive opportunities for farmers, and active lobbying by the organic agriculture movement (especially IFOAM) resulted in the 1991 EU Organic Agriculture Regulation. The Regulation offered an organic production standard and modest procedural rules for private governance schemes. Continued problems due to a fragmented private governance market led the Commission to propose severe limitations on private schemes’ governance space in the early-2000s. Opposition to these proposals by private governance schemes, the organic movement, and key Member States prevented significant public intervention. Nonetheless, both standards and procedural regulations were strengthened in an updated Regulation in 2007 by the introduction of a mandatory EU organic logo and mandatory accreditation of private auditors.

Python is a simple-to-use yet powerful scripting language that allows one to solve data problems of varying scale and complexity. It is also the most used tool in data science and most frequently listed in data science job postings as the requirement. Python is a very friendly and easy-to-learn language, making it ideal for the beginner. At the same time, it is very powerful and extensible, making it suitable for advanced data science needs.