Network science is a broadly interdisciplinary field, pulling from computer science, mathematics, statistics, and more. The data scientist working with networks thus needs a broad base of knowledge, as network data calls for—and is analyzed with—many computational and mathematical tools. One needs good working knowledge in programming, including data structures and algorithms to effectively analyze networks. In addition to graph theory, probability theory is the foundation for any statistical modeling and data analysis. Linear algebra provides another foundation for network analysis and modeling because matrices are often the most natural way to represent graphs. Although this book assumes that readers are familiar with the basics of these topics, here we review the computational and mathematical concepts and notation that will be used throughout the book. You can use this chapter as a starting point for catching up on the basics, or as reference while delving into the book.

Although the theatre industry developed mainly from the nineteenth century onwards, Parisian theatres in Molière’s time already had some aspects of modern commercial entertainment – developing strategies to generate additional income and revenue through private performances, for example. This chapter examines how companies competed to position themselves as leaders in the Parisian market. It assesses the seasonal programming and level of success of the plays that were performed, examining the knock-on effect of increased competition on the Hôtel de Bourgogne in the 1660s, and shows the spending and investment choices of Molière’s troupe. As a commercial enterprise, the troupe aimed to attract Parisian audiences while continuing to please the court. It paid, therefore, particular attention to its facilities and services in the capital, and travelled outside Paris to participate in court festivities. The company had to juggle its duty to the King, for whom sumptuous and expensive entertainments were a means of showing his power and influencing other European courts, and to its bourgeois Parisian clients, who provided it with a regular income and could not, therefore, be neglected. In this respect Molière proved to be a wise man, becoming a wealthy entrepreneur of spectacles.

Chapter 8 assembles for the first time the available information about performance histories of earlier popular opera between 1714 and 1790, aiming to discover whether a ‘core’ or ‘canon’ of these works may legitimately be talked of. The introduction explains sources and methods, followed by tables showing for exactly how long the most-seen works held the stage. ‘The Crisis of 1745’ details the economic difficulties of the Comédie-Française, Comédie-Italienne and Fair theatres which lay behind anticompetitive strategies. Favart’s Acajou was one flashpoint, followed by Anne-Marie Du Boccage’s published attack on the Opéra Comique in 1745. ‘Theatre Politics and the Bouffon Legacy’ concerns the backstage history of rivalry between the Opéra Comique and Comédie-Italienne during the final decade of competition, centred on their responses to a revolution in public taste: enthusiasm for comic intermezzi at the Opéra during 1752–54. The Opéra is shown to have played an increasingly defensive game. ‘Creating a Repertory’ is based on close analysis of revivals of popular opera, seen together with Favart’s new role as programming manager at the Opéra Comique. A distinct core of repeatable works is finally identifiable, though revivals can be shown to have involved updating.

Public administration in Norway and in many other countries has used computers for more than fifty-five years. It is normal and necessary. Of course, it is possible to imagine many more office buildings where thousands of men and women would do all the detailed processing of individual cases that are processed today by computers, but this alternative is not very realistic: Modern taxation systems, national social insurance schemes and management of many other welfare programs would not be feasible without the use of computers and the algorithmic law that is integrated in the software. Thus, the question is not if public administration should apply computer technology, but how this should be done. This chapter deals with important how-to questions.

This chapter explains who the book is for – broadly, anyone learning to program, especially in early programming courses at university or college – and explains its approach.

This chapter outlines the book and lays out important preliminaries. First, it addresses why linguists need to know how to program. Second, it explains why Python is a good choice for a programming language. Third, it describes how this book is different from others. Finally, it outlines the text and explains how to get the most out of it.

Making computers ‘personal’ was as much a problem of user enrolment and education as it was of hardware development. Getting mass-participation computing to work required technology firms to engage with the social organization of user groups. Accounts of this process have tended, however, to ignore the very first “personal computers” to come to market, a series of handheld programmable calculators launched in the mid-1970s. The Whipple Museum’s Hookham Collection of pocket electronic calculators contains both devices and ephemera produced by firms such as Hewlett-Packard that became popular for their capacity to store and load programs that could be traded between users. This soon fed into the organization of collective hobbyist newsletters and groups that exchanged programs and advice, allowing for thousands of professionals to become programmers. In this chapter, I explore the development of infrastructure that characterized the first amateur programming collectives and the moral, monetary, and material economies involved, in an attempt to tackle one of the more intractable questions in the history of modern technology: how did computing become personal?