Rust for Data Science: Performance and Safety in Practice

The book "Rust for Data Science" is a practical guide for data professionals, engineers, and developers who want to go beyond Python and master a high-performance, safe, and predictable tool for working with data — the Rust language. Unlike traditional approaches, Rust allows you to create fast, parallel, and reliable data processing pipelines without sacrificing readability and development convenience.

The book is structured as a gradual immersion: from the basics of the Rust ecosystem for data science to implementing full-fledged projects. You will learn not just to write code, but to design efficient solutions for tasks that require high speed and minimal memory consumption.

Who is this book for?

• Data analysts who want to speed up their pipelines and process gigabytes of data without using Spark.
• Machine learning engineers striving to deploy models into production with minimal latency.
• Python developers looking to master Rust for critical data analysis components.
• Students and researchers interested in modern approaches to data processing.

What will you learn?

• Rust ecosystem for data: libraries Polars (analogous to Pandas), ndarray (NumPy), Serde (serialization), reqwest (HTTP requests), and others.
• Data loading and cleaning: reading CSV, JSON, Parquet, handling missing values, type conversion, filtering, and aggregation.
• Statistical analysis: descriptive statistics, correlations, hypothesis testing, using the statrs and linfa crates.
• Machine learning: linear regression, classification, clustering, feature engineering, and model evaluation.
• Parallel processing: using Rayon, multithreading, safe work with shared data.
• Visualization: plotting with plotters, generating reports.
• Integration with Python: calling Rust from Python via PyO3, mixed projects.

Book features

• Numerous code examples with explanations — from simple scripts to complete applications.
• Comparison with Python: you will see how the same tasks are solved in Rust and when it is justified.
• Projects: building a console log analyzer, a web API for predictions, an ETL pipeline.
• Tips on optimization, memory management, and debugging.
• Practical exercises to reinforce the material.

The book is suitable both for those already familiar with Rust and for beginners — all concepts are explained from scratch, with an emphasis on practical application in data science. You will not just learn the syntax, but learn to think in the Rust paradigm: safely, efficiently, and predictably.

After reading, you will be able to independently develop high-performance data analysis tools, integrate them into existing pipelines, and confidently use Rust in your daily work with data.