A Mathematical Introduction to Data Science Authors: Yi Sun and Rod Adams

A Mathematical Introduction to Data Science by Yi Sun and Rod Adams delivers a rigorous yet accessible exploration of the core mathematical principles powering the field of data science. Ideal for students, researchers, and professionals seeking a robust technical foundation, this book unveils the essential concepts underpinning modern analytical techniques—from linear algebra and probability theory to optimization and algorithmic design.

With clarity and precision, Sun and Adams guide readers through the mathematical structure behind popular machine learning models and data-driven systems. Each chapter blends theoretical discussions with practical examples, ensuring that readers gain both intuitive understanding and hands-on skills.

Key topics include:

Vector spaces, matrices, and eigenvalue problems

Statistical inference and probabilistic models

Gradient descent and optimization frameworks

Clustering, classification, and regression analysis

Real-world case studies bridging theory and application

Published by Springer, this book is particularly valuable for anyone striving to demystify the mathematics that govern predictive analytics and computational learning. Whether you’re building algorithms or interpreting data to influence business decisions, it equips you with the clarity and confidence to apply mathematical logic in the fast-evolving world of data science.