Social data generated digitally (from, for example, social media, communications platforms, Internet of Things (IoT) devices, sensors/wearables, and mobile phones) offer a way to accumulate new large-scale data, in addition to existing data that have been converted to digital formats. These data can be put to work helping us understand big issues of crucial interest to the social sciences, industry, and policy-makers including social, economic and political behaviour, interpersonal relationships, market design, group formation, identity, international movement, ethics and responsible ways to enhance the social value of data, and many other topics. The growing field of social data science involves developing the science of these social data: creating viable datasets out of messy, real world data; critiquing inequalities inherent in the data or manifested through analyses of this data, and developing the tools and techniques to make meaningful claims about the social world, through explanation, prediction and experimentation. In this way, social data science offers a data science where the data relates to individual and social behaviour and a social science with generation and analysis of real-time transactional data at its centre.