Name: Mathematical Methods in Data Science. Bridging Theory and Applications with Python (en Inglés)
SKU: 64691098
Price: 99230.00 CLP
Availability: InStock
Author: Sebastien Roch
ISBN: 9781009509404

portada Mathematical Methods in Data Science. Bridging Theory and Applications with Python (en Inglés)

Mathematical Methods in Data Science. Bridging Theory and Applications with Python (en Inglés)

Formato

Libro Físico

Editorial

Cambridge University Press

Autor

Sebastien Roch

Año

2025

Idioma

Inglés

N° páginas

499

Encuadernación

Tapa Blanda

ISBN13

9781009509404

Mathematical Methods in Data Science. Bridging Theory and Applications with Python (en Inglés)

Sebastien Roch (Autor) · Cambridge University Press · Tapa Blanda

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Reseña del libro "Mathematical Methods in Data Science. Bridging Theory and Applications with Python (en Inglés)"

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\nA hands-on textbook for advanced undergraduates and beginning graduate students in data science, offering an accessible introduction to the essential mathematics behind modern data analysis. With practical Python examples and hundreds of exercises and problems, it's an invaluable resource for students and educators alike.
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\nBridge the gap between theoretical concepts and their practical applications with this rigorous introduction to the mathematics underpinning data science. It covers essential topics in linear algebra, calculus and optimization, and probability and statistics, demonstrating their relevance in the context of data analysis. Key application topics include clustering, regression, classification, dimensionality reduction, network analysis, and neural networks. What sets this text apart is its focus on hands-on learning. Each chapter combines mathematical insights with practical examples, using Python to implement algorithms and solve problems. Self-assessment quizzes, warm-up exercises and theoretical problems foster both mathematical understanding and computational skills. Designed for advanced undergraduate students and beginning graduate students, this textbook serves as both an invitation to data science for mathematics majors and as a deeper excursion into mathematics for data science students.