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035   $a(DE-He213)978-3-031-89707-8
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100   $a20250527d2025      u|         0
101 0 $aeng
135   $aurcnu||||||||
181   $ctxt$2rdacontent
182   $cc$2rdamedia
183   $acr$2rdacarrier
200 10$aMath for Data Science /$fby Omar Hijab
205   $a1st ed. 2025.
210  1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2025.
215   $a1 online resource (588 pages)
311 08$a3-031-89706-4 
327   $aPreface -- List of Figures -- Datasets -- Linear Geometry -- Principal Components -- Calculus -- Probability -- Statistics -- Machine Learning -- A. Auxiliary Material -- B. Auxiliary Files -- References -- Python Index -- Index.
330   $aMath for Data Science presents the mathematical foundations necessary for studying and working in Data Science. The book is suitable for courses in applied mathematics, business analytics, computer science, data science, and engineering. The text covers the portions of linear algebra, calculus, probability, and statistics prerequisite to Data Science. The highlight of the book is the machine learning chapter, where the results of the previous chapters are applied to neural network training and stochastic gradient descent. Also included in this last chapter are advanced topics such as accelerated gradient descent and logistic regression trainability. Clear examples are supported with detailed figures and Python code; Jupyter notebooks and supporting files are available on the author's website. More than 380 exercises and nine detailed appendices covering background elementary material are provided to aid understanding. The book begins at a gentle pace, by focusing on two-dimensional datasets. As the text progresses, foundational topics are expanded upon, leading to deeper results at a more advanced level.
606   $aMathematics
606   $aArtificial intelligence$xData processing
606   $aApplications of Mathematics
606   $aData Science
606   $aDades massives$2thub
606   $aMatemātica$2thub
606   $aAplicacions industrials$2thub
608   $aLlibres electrōnics$2thub
615  0$aMathematics.
615  0$aArtificial intelligence$xData processing.
615 14$aApplications of Mathematics.
615 24$aData Science.
615  7$aDades massives
615  7$aMatemātica
615  7$aAplicacions industrials
676   $a519
700   $aHijab$b O$058868
801  0$bMiAaPQ
801  1$bMiAaPQ
801  2$bMiAaPQ
906   $aBOOK
912   $a9911007459903321
996   $aMath for Data Science$94389846
997   $aUNINA