C++ for Data Science: From Raw Data to Production Algorithms

This book is your bridge between the power of C++ and the world of Data Science. In an era where Python dominates prototyping, C++ remains an indispensable language for production systems where speed, memory efficiency, and real-time data stream processing are critical. The author, Artem Voronov — an engineer with 10 years of experience in high-load systems and ML infrastructure — systematically reveals how to apply modern C++ (C++17/20 standards) to build high-performance data processing pipelines.

The book starts with the basics: you will learn to efficiently work with STL containers for storing and transforming large datasets, master move-semantics and smart pointer idioms to prevent memory leaks, and get acquainted with parallel algorithms from the standard library. Then you will move on to specialized tasks: implementing basic machine learning algorithms (linear regression, k-nearest neighbors, naive Bayes classifier) without using heavy frameworks — only with STL and Eigen for linear algebra.

Special attention is given to working with large volumes of data that do not fit into RAM. You will learn how to write stream processors, use memory-mapped files, and build distributed pipelines using MPI. A separate chapter is dedicated to integrating C++ with Python via pybind11 — this will allow you to create fast extension modules for existing Python scripts.

Practical examples include: building a recommendation system based on collaborative filtering, implementing a feedforward neural network from scratch, optimizing ETL processes, and creating your own library for working with time series. All examples are accompanied by benchmarks and profiling using perf and Valgrind.

Who this book is for:

• Data scientists who want to speed up their models and move from prototypes to production code
• C++ developers seeking to apply their skills in analytics and machine learning
• Students in technical fields studying high-performance computing
• Data engineers working with real-time systems and IoT

What the reader will learn:

• How to choose data structures for a specific analysis task
• Code optimization techniques at the compiler and algorithm level
• Methods for parallelizing computations (OpenMP, TBB, std::thread)
• How to profile and debug the performance of C++ programs
• Ways to integrate C++ code into the Python ecosystem
• Implementation of key ML algorithms without ready-made libraries

The book assumes basic knowledge of C++ (syntax, classes, templates) and a general understanding of statistics. The author's approach is from simple to complex: each new concept is reinforced with working code that can be immediately run and modified. This is not a reference book, but a practical guide for those who want to write fast and reliable programs for working with data in one of the most powerful programming languages.