Fundamentals of Information Theory, Source, and Channel Coding

Modern digital communication and data storage rely on mathematical principles that govern how information is quantified, compressed, and transmitted reliably across noisy channels. This text-based course introduces the core foundations of information theory, guiding you step-by-step through the essential concepts without unnecessary complexity. You will build an intuitive and mathematical understanding of information measures, discover how data compression algorithms remove redundancy, and learn how error-correcting codes protect messages from transmission errors. What you'll learn: - Understand fundamental concepts including information measures, Shannon entropy, mutual information, and divergence - Calculate source entropy and limits on lossless data compression using source coding theorems - Apply practical source coding techniques such as Huffman coding and run-length encoding to compress text and binary data - Evaluate discrete memoryless channels, transition probabilities, and channel capacity bounds - Construct linear block codes, parity-check matrices, and Hamming codes for error detection and correction - Explore modern applications of coding theory in contemporary digital communications, storage systems, and data pipelines The course begins with fundamental definitions and probability concepts before methodically moving into source compression techniques, channel modeling, and error-correction strategies through clear written explanations and practical exercises. This course is designed for aspiring engineers, computer science students, data practitioners, and curious learners looking to establish a strong foundational understanding of communication theory from scratch. Start reading today to master the mathematical principles behind modern digital communication.