Real-world data sets are messy and complicated. Written for students, researcher and academician in social science and public management, this authoritative but approachable guide describes all the tools needed to collect data and prepare it for analysis. Offering detailed, step-by-step instructions, it covers the collection of many different types of data, including web files, APIs, and maps; data cleaning; data formatting; the integration of different sources into a comprehensive data set; and storage using third-party tools to facilitate access and shareability, from Google Docs to Git Hub. Assuming no prior knowledge of R and Python, the author introduces programming concepts gradually, using real data sets that provide the reader with practical, functional experience.

Social data science is a new discipline combining the social sciences and computer science in which the analysis of big data is linked to social scientific theory and analysis. These enormous sets of data can also be combined with qualitative data collected through anthropological fieldwork etc.