Expanding the scope of statistical computing: Training statisticians to be software engineers

Traditionally, statistical computing courses have taught the syntax of a particular programming language or specific statistical computation methods. Since the publication of Nolan and Temple Lang (2010), we have seen a greater emphasis on data wrangling, reproducible research, and visualization. This shift better prepares students for careers working with complex datasets and producing analyses for multiple audiences. But, we argue, statisticians are now often called upon to develop statistical software, not just analyses, such as R packages implementing new analysis methods or machine learning systems integrated into commercial products. This demands different skills. We describe a graduate course that we developed to meet this need by focusing on four themes: programming practices; software design; important algorithms and data structures; and essential tools and methods. Through code review and revision, and a semester-long software project, students practice all the skills of software engineering. The course allows students to expand their understanding of computing as applied to statistical problems while building expertise in the kind of software development that is increasingly the province of the working statistician. We see this as a model for the future evolution of the computing curriculum in statistics and data science.Comment: 22 page

Analysis of the behavior of Airbnb in four different Spanish areas

The appearance and rise of the Airbnb business has created a huge amount of debate and a new way (and a useful tool) to understand tourism. Being Spain highly dependent on this sector, by using the data provided by the website InsideAirbnb this essay tries to analyze the behavior of the business (and consequently, of tourism) in four Spanish areas that have different characteristics: Euskadi, Madrid, Málaga and Mallorca. Results suggest that the areas differ in the type of properties they offer, have similar peaks on tourism during certain months (except for Madrid), and that the prices depend on the size of the accommodation as well as on its location. It also shows that what customers value most is not the price, but the location, cleanliness and the value perceived