This book provides a comprehensive foundation in the mathematical principles of information theory and their professional application in communications, electrical engineering, and computer science. The text establishes a rigorous theoretical framework, beginning with the fundamental concepts of Information Theory--such as entropy and mutual information--to explain the physical and mathematical necessity for efficient coding. It then provides an in-depth exploration of the two primary pillars of coding theory: Source Coding, including advanced quantization (analog-to-digital conversion) and high-efficiency compression (approaching the theoretical limits of interpretation), and Channel Coding for error detection and correction. The third part of the book integrates Data Security, covering modern cryptographic algorithms and secure network protocols essential for protecting information integrity. To a multidisciplinary extent, the content also explores the intersections of coding with Artificial Intelligence (Transformers and LSTMs) and the biological "Coding Brain." Designed for a professional and academic audience, this book is an essential resource for those developing projects in data science, industrial digital networks, IoT, and Big Data management. Through its lab-based approach and practical templates, it bridges the gap between high-level theory and real-world implementation.
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