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.

José Unpingco, Ph.D., holds a doctorate in electrical engineering with a specialization in intelligent systems from UC San Diego (1998). In addition to his academic credentials, he is the author of multiple Springer textbooks on Python, data analysis, machine learning, and signal processing, along with numerous peer‑reviewed and conference publications across machine learning, high‑performance computing, and scientific computing. His educational foundation is deeply intertwined with contributions to technical literature and curriculum development.

Over a 25+ year career, Unpingco has held senior technical and leadership roles across academia, health_care, defense, and enterprise software. Since 2017, he has served as a senior lecturer at UC San Diego, winning the 2020 ECE Lecturer of the Year award and creating cornerstone data science and programming courses taken by over 1,600 students. In industry, he currently works as a senior staff AI/machine learning engineer at ServiceNow.