Go (Golang) for Data Science: A Practical Guide

The book "Go (Golang) for Data Science" is the first practical guide in Russian that shows how to use Go for data analysis, machine learning, and visualization tasks. Contrary to the common belief that data science is the prerogative of Python and R, Go offers unique advantages: high performance, built-in concurrency support, and ease of deployment.

The book is intended for developers who are already familiar with the basics of Go and want to expand their skills in data science. It will also be useful for data professionals who are looking for a faster alternative to Python for production systems. A basic understanding of Go and statistics is sufficient for reading.

From the book you will learn:

• How to efficiently read, process, and clean data using the standard library and packages encoding/csv, encoding/json
• How to use libraries for numerical computations: gonum (matrices, linear algebra, statistics)
• How to build visualizations in Go using plot and go-echarts
• How to implement machine learning algorithms: linear regression, k-nearest neighbors, k-means clustering
• How to use concurrency (goroutines, channels) to speed up processing of large datasets
• How to create production-ready data pipelines in Go
• How to integrate Go models with web services and databases

Each chapter contains practical examples and exercises. You will start with simple data loading tasks, then move on to statistical analysis, visualization, and finally to creating full-fledged ML models. Special attention is paid to comparing the performance of Go and Python on real-world tasks.

By the end of the book, you will be able to:

• Write efficient data science scripts in Go
• Build interactive charts and dashboards
• Implement machine learning algorithms from scratch
• Develop high-performance data processing systems

This book is a bridge between the world of Go and data science. It will show that Go is not only a language for backend development but also a powerful tool for working with data, especially when speed and scalability are important.