Mathematical Methods in Data Science (Cambridge Mathematical Textbooks)

by [color=var(--__dChsbGMXsw4B,#2162a1)]Sebastien Roch (Author)  [color=var(--__dChNmAwPaMXsw4B,#565959) !important]Format: Paperback

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Bridge 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.




要求：非转换的PDF格式版本，作者是Sebastien Roch（不是其他作者的同名书籍）。