The world has changed in the information space over the last two decades due to three main factors: firstly there has been the routine deployment of high content measurement devices, from personal photos to satellite images to DNA sequencing to social media feeds. Secondly we have had the network and disk to store information at
scale, often storing information that we don’t know the value of. Thirdly increasingly sophisticated computational techniques, given labels such as “data science,” “machine learning,” and “AI,” have been developed. All these phenomena can be collected under the heading of “Big Data.” This book provides an overview of these trends and the practical ways to handle this. Much of the complexity of dealing with data at this scale is about engineering—the practicalities about whether one can manage data flows robustly and cheaply—as well as the more statistically and algorithmically sophisticated analysis schemes. Herethe reader can learn about both, and see this from a generic perspective of how to transmit, store, and organise data through to more subjectspecific topics such as an introduction to Big Data approaches in bioinformatics. The book is designed for a broad audience, applicable to seasoned computational and data scientists as well as people at the start of their careers. The authors have provided both overviews and practical examples.
