Data is increasingly accessible in large quantities, and is often a side product of regular activities. Companies such as Facebook have access to detailed behaviour logs from millions of users, as do operators of smart grids or health services. This paradigmatic shift from limited, often purpose-generated data to vast amounts of incidental data has been termed 'big data'.
Big data raises questions on a technical level, requiring basic infrastructural and novel analytical techniques. Business utilising big data can be found throughout the digital economy. Big data is also highly relevant for policy, for example in public health, energy and environmental protection and traffic and urban planning; as well as to research in the sciences, social sciences, and humanities. However, these opportunities also raise ethical concerns, most prominently in the realm of privacy.
The Big Data course is one of AUC's 'Big Questions' courses, which focus on broad questions in an interdisciplinary framework. It is built around the notion of a paradigm-shift towards big data, and proceeds through four stages:
- Philosophical: concepts and contexts, which introduce the Kuhnian theory of paradigm shifts and discuss the history of technology, computation, and information.
- Technological: from data to information, discussing the collection, storage, analysis, and modelling of data, and applications in the sciences. This section also lays down fundamental skills.
- Social: power shifts and case studies, which focus on the power shifts resulting from the paradigm shift towards big data through case studies on businesses, government policies, and the digital humanities.
- Universal: criticism and big issues, encompassing critical thinking about and analysis of technical, legal and moral dilemmas.
The course Big Data aims to foster an appreciation of the opportunities brought about by big data, providing students with a framework within which to approach novel questions in all academic fields, business and the arts. At the same time, it emphasises critical thinking about the ethical, social, and technological issues engendered by big data.