Math for Data Science

Erscheinungsjahr 2025
ISBN: 978-3-031-89706-1
Verlag: Springer

Buch, Englisch, 575 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 1150 g

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.

Zielgruppe

Upper undergraduate

Autoren/Hrsg.

Hijab, Omar

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle

Inhaltsverzeichnis

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

Über Autor(innen)

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 I, 4th edition (978-3-319-28399-9) and (978-0-387-96384-6).