000			nam a22 4500
999			_c33213 _d33213
008			240501b xxu||||| |||| 00| 0 eng d
020			_a9780443186790
082			_a005.7 _bREN
100			_aRen, Jingl
245			_aMathematical methods in data science
260			_bElsevier, _c2023 _aAmsterdam :
300			_axi, 246 p. ; _bill. (some col.), _c23 cm.
365			_b200.00 _c$ _d86.30
504			_aIncludes bibliographical references and index.
520			_aMathematical Methods in Data Science covers a broad range of mathematical tools used in data science, including calculus, linear algebra, optimization, network analysis, probability and differential equations. Based on the authors' recently published and previously unpublished results, this book introduces a new approach based on network analysis to integrate big data into the framework of ordinary and partial differential equations for dataanalysis and prediction. With data science being used in virtually every aspect of our society, the book includes examples and problems arising in data science and a clear explanation of advanced mathematical concepts, especially data-driven differential equations, making it accessible to researchers and graduate students in mathematics and data science.
650			_aMathematical models
650			_aDifferential Equations
650			_aPartial differential equations
650			_aLyapunov exponent
650			_aLinear subspace
650			_aQR decomposition
650			_aLaplacian matrix;
650			_aPDE model
650			_aInfluenza
650			_aGradient descent
650			_aEigenvectors
650			_aChainlet clusters
700			_aWang, Haiyan
942			_2ddc _cBK