東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Math for data science

Hijab, O.

Linked to FindBook

Google Book

Amazon

博客來

Math for data science

Record Type:	Electronic resources : Monograph/item
Title/Author:	Math for data science/ by Omar Hijab.
Author:	Hijab, O.
Published:	Cham :Springer Nature Switzerland : : 2025.,
Description:	xv, 575 p. :ill., digital ;24 cm.
[NT 15003449]:	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
Subject:	Computer science - Mathematics. -
Online resource:	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

Math for data science
LDR:02291nmm a2200325 a 4500 001 2410444
003 DE-He213
005 20250527130241.0
006 m d
007 cr nn 008maaau
008 260204s2025 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 523802
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 532725
650 0 $a Mathematics $x Industrial applications. $3 904684
650 1 4 $a Applications of Mathematics. $3 890893
650 2 4 $a Data Science. $3 3538937
710 2 $a SpringerLink (Online service) $3 836513
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)