The Calculus of Data

Beschreibung

This book provides an essential guide for data science and machine learning practitioners looking to deepen their understanding of abstract mathematical concepts and bridge the gap between coding and theoretical research. By combining mathematical rigor with practical, programmable examples, it transforms complex ideas—such as groups, manifolds, and quotient spaces—into accessible computational frameworks. Perfect for emerging data scientists and engineers, this book empowers readers to move beyond simply using AI tools and equips them with the knowledge to fundamentally understand the principles that shape them.

• Shows how abstract mathematical structures attain concrete form through executable Python models
• Converts theoretical invariants into testable hypotheses via computational scripts for rigorous validation
• Cultivates the structural maturity needed to interpret advanced research as coherent mathematical architecture

This book provides an essential guide for data science and machine learning practitioners looking to deepen their understanding of abstract mathematical concepts and bridge the gap between coding and theoretical research. By combining mathematical rigor with practical, programmable examples, it transforms complex ideas—such as groups, manifolds, and quotient spaces—into accessible computational frameworks. Perfect for emerging data scientists and engineers, this book empowers readers to move beyond simply using AI tools and equips them with the knowledge to fundamentally understand the principles that shape them.

Autor*in

José Unpingco

Themen in »The Calculus of Data«

Data science fundamentals Machine learning theory Mathematical reasoning in data science Abstract mathematics for data science Python data science programming Advanced data analysis techniques Applied mathematics for data scientists Mathematical frameworks in AI Theoretical foundations of machine learning

Stimmen zu »The Calculus of Data«

Details

Link teilen