Mathematical Methods in Data Science 1st Edition

Mathematical Methods in Data Science (1st Edition) by Jingli Ren and Haiyan Wang is a rigorous yet accessible guide that bridges advanced mathematics and practical data science. Designed for graduate students, data scientists, and researchers, this book transforms abstract theory into tools you can apply immediately to machine learning, signal processing, and large-scale analytics.

Start with a clear overview of core mathematical foundations—linear algebra, probability and statistics, optimization, and numerical methods—and progress to applied topics such as dimensionality reduction, spectral analysis, and stability of algorithms. Each chapter emphasizes intuition, worked examples, and problem-solving strategies so complex concepts become usable techniques for real-world datasets.

Readers worldwide will appreciate the book’s balanced approach: formal derivations where needed, followed by practical insights that improve model design, training efficiency, and interpretability. Whether you’re preparing for advanced coursework, building production-level pipelines, or conducting research, this edition equips you with the mathematical fluency essential for modern data science and machine learning.

Packed with clear explanations, illustrative figures, and carefully chosen exercises, Mathematical Methods in Data Science is a reliable reference for academic courses and industry teams alike. From universities to tech labs across North America, Europe, and Asia, this title provides a common mathematical language for interdisciplinary collaboration.

Gain confidence in the math behind algorithms—order your copy of Mathematical Methods in Data Science by Jingli Ren and Haiyan Wang and elevate your quantitative toolkit for practical, scalable data science.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.