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 building production-ready pipelines.

Traditionally, Data Science is associated with Python and R, but Go offers a number of advantages: high performance, static typing, excellent concurrency support, and ease of deployment. This book bridges the gap between a powerful Go backend and the world of data.

Who is this book for?

• Go developers who want to transition into Data Science or complement their skills
• Data Scientists looking to speed up their pipelines and deploy models into production
• DevOps and data engineers working with high-load systems
• Students and anyone interested in modern approaches to data processing

What will you learn?

• Data fundamentals: loading, cleaning, and transformation using packages gonum, gota, and dataframe
• Data visualization in Go: creating charts with go-echarts and plot
• Statistical analysis and linear algebra using gonum
• Implementing machine learning algorithms: linear regression, K-means, decision trees — with and without frameworks
• Working with neural networks and the gorgonia library
• Building production pipelines: model serialization, REST API, integration with Kafka and PostgreSQL
• Parallel processing of big data with goroutines and channels

Book features

• Practical examples and ready-to-use code
• Comparison of Go and Python approaches for each task
• Real-world cases: from log analysis to building a recommendation system
• Tips for optimizing and debugging Data Science projects in Go

After reading, you will be able to build fast and reliable data analysis systems that are easy to scale and maintain. The book is suitable for both beginners in Data Science and experienced engineers looking to expand their toolkit.

Master Go for Data Science — and gain a powerful tool for working with data in a modern high-load environment!