Information Theory and Coding: Fundamentals of Data and Compression

Every byte of data sent over the internet or saved to a hard drive relies on the principles of information theory. Understanding how we quantify, compress, and secure data is essential for building efficient modern software and communication systems. This course guides you from absolute beginner to a solid conceptual understanding of how information is measured and transmitted. You will learn to evaluate data limits, design basic compression algorithms, and apply error-detecting codes to ensure data integrity. What you'll learn: 1. Understand the mathematical concept of entropy to measure information and uncertainty quantitatively. 2. Apply source coding algorithms like Huffman coding to compress data efficiently. 3. Analyze channel capacity limits using Shannon's fundamental theorems of communication. 4. Implement error-correcting and error-detecting codes to protect data from transmission noise. 5. Explore foundational concepts of data security and encryption to prevent unauthorized access. 6. Practice solving coding theory problems through structured, step-by-step written exercises. The course starts with foundational definitions of probability, uncertainty, and entropy before moving into practical coding techniques. You will progress through clear, written explanations of compression, error control, and modern security applications. This course is designed for aspiring computer scientists, data analysts, and software developers who want to grasp the core science of data. No advanced engineering background is required. Start reading today to unlock the fundamental principles that power the digital age.