Introduction to Data Science and Engineering

Abstract

Recent technological advances in sensors, embedded devices, and smart mobile devices and in applications, such as social networks, are making possible to pervasively capture huge amounts of data from many different contexts in both the physical world and the cyber world.Technologies, such as cloud systems, high-performance computing, data analytics, make possible to not only to store huge amounts of dataoften referred to as big data, but also to process data for many different purposes.Big data is poised to make possible applications that in the past were difficult if at all possible, like personalized healthcare, and enhance all aspects of human life and society, ranging from urban environments to energy and transportation infrastructures, and manufacturing.Data is increasingly playing a major role in addressing challenges such as food and water security and assuring the health of the various ecosystems.Data is today critical for scientific research.Many science and engineering areas are currently experiencing from a 100-to a 1000-fold increase in the volumes of generated data compared to only one decade ago.This data is produced by many sources including simulations, high-throughput scientific instruments, satellites, and telescopes.The availability of big data is revolutionizing how research is conducted and is leading to the emergence of a new paradigm of science based on data-intensive computing and analytics.However, unlocking the power of data requires addressing several major challenges.Jagadish et al. [1] articulate a discussion of such challenges based on the notion of data management and analysis pipeline: B

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