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Abstract

The main objective of this chapter is to provide an overview of the modern field of data science and some of the current progress in this field. The overview focuses on two important paradigms: (1) big data paradigm, which describes a problem space for the big data analytics, and (2) machine learning paradigm, which describes a solution space for the big data analytics. It also includes a preliminary description of the important elements of data science. These important elements are the data, the knowledge (also called responses), and the operations. The terms knowledge and responses will be used interchangeably in the rest of the book. A preliminary information of the data format, the data types and the classification are also presented in this chapter. This chapter emphasizes the importance of collaboration between the experts from multiple disciplines and provides the information on some of the current institutions that show collaborative activities with useful resources.

Notes

Acknowledgements

Thanks to the Department of Statistics, University of California, Berkeley; the Center for Science of Information, Purdue University; the Statistical Applied Mathematical Science Institute; and the Institute for Mathematics and its Applications, University of Minnesota for their support which contributed to the development of this book.

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Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Shan Suthaharan
    • 1
  1. 1.Department of Computer ScienceUNC GreensboroGreensboroUSA

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