A Practical Guide to Optimization in Engineering and Data Science

This book offers a hands-on and comprehensive guide to optimization techniques tailored for data scientists and engineers, combining theoretical foundations with practical applications. It begins by demystifying core concepts and types of optimization, then explores their relevance across engineering and data science domains. Readers are introduced to essential mathematical tools, single- and multi-objective optimization methods, and a wide range of algorithms including gradient-based techniques, evolutionary strategies, and swarm intelligence. The book also lists real-world applications across industries and provides several Python-based examples, enabling readers to implement and experiment with optimization models in practice. With its structured approach and rich set of examples, this book serves as a valuable resource for professionals and researchers seeking to apply optimization effectively in their work.

Presents Industry 4.0 optimization techniques for navigating the evolving engineering and data science landscapes Focuses on practical examples and explanations by analogies, rather than mathematical concepts Covers single-objective and multi-objective optimization, equipping readers for success in diverse industry settings