C++ for Data Science: Computing on the Edge of Possibility

The world of Data Science is traditionally associated with Python and R. But when it comes to processing terabytes of data at speed, training models on huge datasets, or deploying solutions in production with strict latency requirements — C++ takes the stage. This book is a bridge between the world of the data researcher and the world of systems programming.

The book "C++ for Data Science" does not teach C++ from scratch. It assumes the reader is already familiar with the basics of the language (pointers, STL, OOP) and has basic experience with Python and libraries like Pandas or NumPy. The main focus is on those aspects of C++ that are critically important for building efficient data processing and machine learning pipelines.

What you will learn from the book:

• Memory Management and Performance: How to avoid data copying, use move semantics, smart pointers, and memory pools for working with datasets that don't fit in RAM.
• Custom Data Structures: Designing and implementing custom containers (Sparse Matrix, DataFrame, Tensor) from scratch, optimized for specific tasks.
• Parallel Computing: Using std::thread, OpenMP, and Intel TBB to parallelize data transformations, aggregations, and cross-validation.
• Integration with Python: A practical guide to wrapping C++ code into Python modules using Pybind11. You will learn to write libraries that are called from Python like regular functions but run 50-100 times faster.
• Machine Learning in C++: Implementing algorithms from scratch: linear regression (via Eigen), gradient boosting (without Sklearn), neural networks (minimal forward/backward pass).
• Working with Big Data: Reading and writing binary formats (HDF5, Parquet), memory-mapping files (mmap), streaming data processing without loading everything into RAM.

Who this book is for:

• Data Science engineers who want to speed up their data processing pipelines.
• C++ developers transitioning into the field of data analysis and machine learning.
• Technical students studying high-performance computing.
• Anyone who has encountered the limitations of Python when working with large volumes of data.

After reading, you will be able not only to read and understand other people's C++ code in ML frameworks (TensorFlow, XGBoost, FAISS) but also to create your own high-performance components. You will learn to think at the hardware level: cache misses, data alignment, vectorization — all of this will become part of your working toolkit.

The book contains many ready-to-use code examples that can be immediately applied in real projects. Each chapter concludes with a practical assignment and a list of references for further study.

Why should you read this book right now? With the growth of data volumes and model complexity (LLMs, recommendation systems), the ability to write efficient C++ code is becoming not just an advantage, but a necessity. Python is excellent glue, but C++ is the engine. Give your Data Science project a turbo boost.