Probability and Statistics in Examples and Tasks . Volume 3. The Theory of Information and Coding by Kelbert M. I. and Suhova Y. M. is a fascinating and deep dive into the world of information theory and coding, which makes it an indispensable tool for students, graduate students and anyone who seeks to expand their knowledge in the field of statistics and probability. This volume is the third part of an extensive cycle that covers key aspects of probability theory and statistics, and like previous issues, offers readers not only theoretical foundations but also practical examples, making learning more accessible and engaging. In this volume, the authors focus on important topics such as entropy, mutual information, source and channel coding, and various methods of transmitting and storing information. These concepts are fundamental to understanding modern technologies, such as data compression, cryptography, and data transmission over communication channels. The book not only outlines the theory, but also offers a variety of tasks that will help readers consolidate their knowledge and develop analytical thinking. Who can like this edition? First of all, the book will be of interest to technical students studying mathematics, computer science and related disciplines . It can also be useful for graduate students and researchers working in the field of information theory and statistics. Even professionals seeking to update their knowledge or delve into new aspects of their activities will find much useful in this volume. The book is written in accessible language, which makes it suitable for a wide audience, including those who are just beginning to get acquainted with this topic. One of the key topics raised in the book is the relationship between information theory and practical applications in modern technology. The authors explain how the principles set forth in information theory can be applied to real-world problems, such as optimizing data transfer and creating efficient codes. This makes the book not only a tutorial, but also a practical guide for professionals working in the field of information technology. The style of the authors is distinguished by clarity and logical presentation, which allows the reader to easily assimilate complex concepts. Kelbert M. I. and Yu . Sukhov M. are known for their scientific works and teaching aids, which are popular among students and teachers. Their previous work, such as "Probability and Statistics in Examples and Tasks. Volume 1 and Volume 2 have already won acclaim for their accessibility and depth of presentation, and this volume is no exception. Probability and Statistics in Examples and Tasks . Volume 3. Information Theory and Coding" is not just a textbook, but a real guide to the world of information and its coding. It will help readers not only to master the theoretical foundations, but also to learn how to apply them in practice. If you are looking for material that combines scientific rigor and practical focus, then this publication will be a real find for you. Do not miss the opportunity to deepen your knowledge and discover new horizons in the field of statistics and information theory