國立高雄大學圖資館 |

Language: English

Back

Math for data science

Record Type:	Electronic resources : Monograph/item
Title/Author:	Math for data scienceby Omar Hijab.
Author:	Hijab, O.
Published:	Cham :Springer Nature Switzerland :2025.
Description:	xv, 575 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Computer scienceMathematics.
Online resource:	https://doi.org/10.1007/978-3-031-89707-8
ISBN:	9783031897078$q(electronic bk.)

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$q(electronic bk.)

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

181991
Computer science
--Mathematics.

LC Class. No.: QA76.9.M35

Dewey Class. No.: 004.0151

Math for data science
LDR:02292nmm a2200325 a 4500 001 678157
003 DE-He213
005 20250527130241.0
006 m d
007 cr nn 008maaau
008 251021s2025 sz s 0 eng d
020 $a 9783031897078$q(electronic bk.)
020 $a 9783031897061$q(paper)
024 7 $a 10.1007/978-3-031-89707-8 $2 doi
035 $a 978-3-031-89707-8
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.M35
072 7 $a PBW $2 bicssc
072 7 $a MAT003000 $2 bisacsh
072 7 $a PBW $2 thema
082 0 4 $a 004.0151 $2 23
090 $a QA76.9.M35 $b H639 2025
100 1 $a Hijab, O. $3 542251
245 1 0 $a Math for data science $h [electronic resource] / $c by Omar Hijab.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2025.
300 $a xv, 575 p. : $b ill., digital ; $c 24 cm.
505 0 $a Preface -- List of Figures -- Datasets -- Linear Geometry -- Principal Components -- Calculus -- Probability -- Statistics -- Machine Learning -- A. Auxiliary Material -- B. Auxiliary Files -- References -- Python Index -- Index.
520 $a 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.
650 0 $a Computer science $x Mathematics. $3 181991
650 0 $a Mathematics $x Industrial applications. $3 210991
650 1 4 $a Applications of Mathematics. $3 273744
650 2 4 $a Data Science. $3 913495
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-031-89707-8
950 $a Mathematics and Statistics (SpringerNature-11649)