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Math for data science

Hijab, O.

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Math for data science

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Math for data science/ by Omar Hijab.
作者:	Hijab, O.
出版者:	Cham :Springer Nature Switzerland : : 2025.,
面頁冊數:	xv, 575 p. :ill., digital ;24 cm.
內容註:	Preface -- List of Figures -- Datasets -- Linear Geometry -- Principal Components -- Calculus -- Probability -- Statistics -- Machine Learning -- A. Auxiliary Material -- B. Auxiliary Files -- References -- Python Index -- Index.
Contained By:	Springer Nature eBook
標題:	Computer science - Mathematics. -
電子資源:	https://doi.org/10.1007/978-3-031-89707-8
ISBN:	9783031897078

Math for data science
Hijab, O.

Math for data science[electronic resource] /by Omar Hijab. - Cham :Springer Nature Switzerland :2025. - xv, 575 p. :ill., digital ;24 cm.

Preface -- List of Figures -- Datasets -- Linear Geometry -- Principal Components -- Calculus -- Probability -- Statistics -- Machine Learning -- A. Auxiliary Material -- B. Auxiliary Files -- References -- Python Index -- Index.

Math 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.

ISBN: 9783031897078

Standard No.: 10.1007/978-3-031-89707-8doiSubjects--Topical Terms:

532725
Computer science
--Mathematics.

LC Class. No.: QA76.9.M35

Dewey Class. No.: 004.0151

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