Math for Data Science

Omar Hijab

ISBN 10: 3031897064 / ISBN 13: 9783031897061

Published by Springer Nature Switzerland, Springer International Publishing, 2025

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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 not Elektronisches Buch 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.

Seller Inventory # 9783031897061

Bibliographic Details

• Title: Math for Data Science
• Publisher: Springer Nature Switzerland, Springer International Publishing
• Publication Date: 2025
• Language: English
• ISBN 10: 3031897064
• ISBN 13: 9783031897061
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 160H x 36D millimeters 241 millimeters width by 160 millimeters height by 36 millimeters depth
• Weight: 1150 grams

About this title

Synopsis

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.

About the Author

Omar Hijab obtained his doctorate from the University of California at Berkeley, and is faculty at Temple University in Philadelphia, Pennsylvania. Other book publications include Introduction to Calculus and Classical Analysis, 4th edition (978-3-319-28399-9) and Stabilization of Control Systems (978-0-387-96384-6).