Fundamentals of Information Theory and Data Encoding

Information theory forms the backbone of modern digital communications, computer systems, and data processing. This written course provides a beginner-friendly introduction to entropy, data compression algorithms, and communication channels without requiring advanced mathematical prerequisites. What you will learn: Understand core concepts of information, entropy, and probability in data systems. Learn key lossless compression algorithms including Huffman coding and run-length encoding. Analyze signal noise, channel capacity, and error-correction techniques for reliable transmission. Explore encoding principles applied to big data storage formats and image processing workflows. Discover modern data representations and vector embeddings used in modern computing. The course begins with clear definitions of key terminology and theoretical foundations before moving into step-by-step algorithmic explanations and written code examples. Designed for beginners in computer science, aspiring data engineers, and developers looking to strengthen their technical knowledge. Begin reading today to master the underlying mechanics of modern digital communication.