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Mots clés : Data Science, Big Data, Data Driven decision making, 

Provst F and Fawcett T,explain how Data Science works wih Big Data. They show us how this data can be collected, used and analyzed to drive decision making.

Développement :

With vast amounts of data now available, companies in almost every industry are focused on exploiting data for competitive advantage. The volume and variety of data have far outstripped the capacity of manual analysis, and in some cases have exceeded the capacity of conventional databases.

At the same time, computers have become far more powerful, networking is ubiquitous, and algorithms have been developed that can connect datasets to enable broader and deeper analyses than previously possible.

The convergence of these phenomena has given rise to the increasingly widespread business application of data science. Companies across industries have realized that they need to hire more data scientists. Academic institutions are scrambling to put together programs to train data scientists. Publications are touting data science as a hot career choice and even ‘‘sexy.’’

The authors argue that there are good reasons why it has been hard to pin down what exactly is data science. One reason is that data science is intricately intertwined with other important concepts, like big data and data-driven decision making, which are also growing in importance and attention. Another reason is the natural tendency, in the absence of academic programs to teach one otherwise, to associate what a practitioner actually does with the definition of the practitioner’s field; this can result in overlooking the fundamentals of the field.

Data-science academic programs are being developed, and in an academic setting we can debate its boundaries. However, in order for data science to serve business effectively, it is important  to understand its relationships to these other important and closely related concepts, and (to begin to understand what are the fundamental principles underlying data science.

They present a perspective that addresses all these concepts by highlighting the data science as the connective tissue between data-processing technologies and data-driven decision making.

Conclusion :

Underlying the extensive collection of techniques for mining data is a much smaller set of fundamental concepts comprising data science. In order for data science to flourish as a field, rather than to drown in the flood of popular attention, we must think beyond the algorithms, techniques, and tools in common use. We must think about the core principles and concepts that underlie the techniques, and also the systematic thinking that fosters success in data-driven decision making. These data science concepts are general and very broadly applicable

Success in today’s data-oriented business environment requires being able to think about how these fundamental concepts apply to particular business problems—to think data-analytically. This is aided by conceptual frameworks that themselves are part of data science.

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