01_introduction.Rmd

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# Introduction

Lower computing costs, a stark decrease in storage costs for digital data, as well as the diffusion of the Internet, have led to the development of new products (e.g., smartphones) and services (e.g., web search engines, cloud computing) over the last few decades. A side product of these developments is a strong increase in the availability of digital data describing all kinds of everyday human activities [@einav_levin2014; @matter_stutzer2015]. As a consequence, new business models and economic structures are emerging with data as their core commodity (i.e., AI-related technological and economic change). For example, the current hype surrounding 'Artificial Intelligence' (AI) - largely fueled by the broad application of machine-learning techniques such as 'deep learning' (a form of artificial neural networks) - would not be conceivable without the increasing abundance of large amounts of digital data on all kind of socio-economic entities and activities. In short, without understanding and handling the underlying data streams properly, the AI-driven economy cannot function. The same rationale applies, of course, to other ways of making use of digital data. Be it traditional big data analytics or scientific research (e.g., applied econometrics).


The need for proper handling of large amounts of digital data has given rise to the interdisciplinary field of ['Data Science'](https://en.wikipedia.org/wiki/Data_science) and increasing demand for 'Data Scientists'. While nothing within Data Science is particularly new on its own, the combination of skills and insights from different fields (particularly Computer Science and Statistics) has proven to be very productive in meeting new challenges posed by a data-driven economy. In that sense, Data Science is rather a craft than a scientific field. As such, it presupposes a more practical and broader understanding of the data than traditional Computer Science and Statistics from which Data Science borrows its methods. This book focuses on the skills necessary for *acquiring, cleaning, and manipulating* digital data for research/analytics purposes.

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