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To excel in competitive computer science and engineering exams, you need a rock-solid grasp of how data is quantified, compressed, and transmitted. Information theory forms the mathematical backbone of modern communications and data storage. This text-based course guides you from absolute beginner to confidently solving complex exam-style questions by building a deep intuitive understanding of how information is measured and how to design efficient codes. You will learn to: * Understand foundational concepts of entropy, joint entropy, and mutual information * Calculate source coding efficiency and design optimal codes using Huffman and Shannon-Fano algorithms * Analyze channel capacity and apply the Shannon-Hartley theorem to communication channels * Solve step-by-step practice problems modeled after competitive engineering exams * Explore modern applications of information theory in machine learning and data science Starting with basic probability and definitions of information, you will progress through source coding theorems, channel models, and rigorous problem-solving techniques. This course is designed for computer science students, engineering candidates preparing for competitive exams, and beginners wanting a structured mathematical introduction to information theory. No prior advanced engineering background is required. Start reading today to master the core principles of information theory.