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Overview of Basic Methods for Data Science

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Mathematical Problems in Data Science

Abstract

Data science utilizes all mathematics and computer sciences. In this chapter, we give a brief review of the most fundamental concepts in data science: graph search algorithms, statistical methods especially principal component analysis (PCA), algorithms and data structures, and data mining and pattern recognition. This chapter will provide an overview for machine learning in relation to other mathematical tools. We will first introduce graphs and graph algorithms, which will be used as the foundation of a branch of artificial intelligence called search. The other three branches of artificial intelligence are learning, planning, and knowledge representation. Classification, also related to machine learning, is at the center of pattern recognition, which we will discuss in Chap. 4 Statistical methods especially PCA and regression will also be discussed. Finally, we introduce concepts of data structures and algorithm design including online search and matching.

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Authors and Affiliations

  1. The University of the District of Columbia, Washington, DC, USA

    Li M Chen

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  1. Li M Chen
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Correspondence to Li M Chen .

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Chen, L.M. (2015). Overview of Basic Methods for Data Science. In: Mathematical Problems in Data Science. Springer, Cham. https://doi.org/10.1007/978-3-319-25127-1_2

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  • DOI: https://doi.org/10.1007/978-3-319-25127-1_2

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