This special issue brings together a number of articles focusing on the practical side of data science and data analysis. While there has been much hand-wringing about the rise of data science and its relationship to statistics, we avoid all such discussion here. Instead, the articles address the many aspects of day-to-day analytical work that are almost absent from conventional statistics literature and curriculum. Anyone who has ever taken wild-caught data through the full process of analysis knows that “statistics,” in the strict sense of fitting models and doing inference, is but one small part of the process. The remainder of the process accounts for a considerable share of the time and effort of data analysts, data scientists, and applied statisticians, but this is generally ignored in the statistics literature. We want to shine some light on these important areas.