The book "Methods of processing experimental data", written by a team of authors under the leadership of I. In. Retinska is an important work that will be useful for both students and professional researchers in the field of science and technology .. This publication is a real guide to modern methods of data analysis, which are becoming increasingly relevant in the context of the rapid development of technology and an increase in the volume of information. From the first pages of the book, the reader plunges into the world of statistics and data processing. The authors offer readers not only theoretical foundations, but also practical examples, which makes the material accessible and understandable. The book covers a wide range of methods, including classical statistical approaches, as well as modern machine learning algorithms and big data processing. This allows the reader not only to understand how to properly collect and process data, but also how to interpret the results, which is especially important in scientific research. Who can like this edition? First of all, the book will be of interest to students and graduate students studying statistics, mathematics, computer science and related disciplines . It will also be a useful resource for researchers, analysts and data scientists who seek to improve their skills in processing and analyzing experimental data. If you are passionate about science, want to understand complex methods of data analysis or just looking for a way to improve your research skills, this publication will become an indispensable assistant for you. The topics raised in the book cover many aspects, ranging from the basics of statistics to complex methods of analysis. The authors emphasize the importance of choosing the right methods depending on the type of data and the goals of the study. They discuss important issues such as missing values processing, hypothesis testing, model building, and data visualization. Each chapter contains examples and assignments that will help the reader to consolidate the knowledge gained in practice. The writing style of the authors is clear and accessible, which makes it easy to assimilate even the most complex concepts. The book is written taking into account the needs of the modern reader: there are many illustrations, graphs and tables that make the material more visual. The authors use relevant examples from various fields of science, which helps the reader to see the practical application of the studied methods. In addition, the team of authors includes specialists with extensive experience in the field of data processing, which gives the book additional value. Their previous work has also received recognition in academia, and this publication was no exception. If you are familiar with other works of the authors, then "Methods of processing experimental data" will be a logical continuation of your acquaintance with their works. In conclusion, Methods for Processing Experimental Data is not just a tutorial, but a whole world of possibilities for those who want to understand more deeply how data analysis works. It inspires new research and experiments, opening the door for the reader to the world of science. If you are looking for a high-quality and informative guide to data processing, this publication will become your reliable companion in scientific research. Do not miss the chance to expand your knowledge and skills in this relevant and important area!