C# (.NET) for Data Science: From Basics to Production Models

The world of data science is traditionally associated with Python, but the .NET platform offers powerful and mature tools for building data pipelines, machine learning, and analytics. The book "C# (.NET) for Data Science" is the first comprehensive guide in English that proves: C# is a full-fledged language for data science tasks, especially in corporate environments where the Microsoft stack is already in use.

What is this book about?

The book covers the complete data lifecycle in the .NET ecosystem:

• Data Collection and Cleaning: working with CSV, JSON, databases (SQL Server, PostgreSQL), stream processing with LINQ and the DataFrame library from Microsoft.Data.Analysis.
• Statistical Analysis and Visualization: using ML.NET, MathNet.Numerics for statistical tests, creating charts with Plotly.NET and LiveCharts2.
• Machine Learning: training classification, regression, clustering, and recommendation system models with ML.NET. Exporting models to ONNX for cross-platform use.
• Deep Learning: integration with TensorFlow.NET and TorchSharp for neural networks.
• Production Deployment: packaging models into web services (ASP.NET Core), creating console utilities, pipelines in Azure ML, and Docker containers.
• Integration with Python: calling Python scripts from C#, using Python.NET, and collaborative work between the two ecosystems.

Who is this book for?

• .NET developers who want to learn data science without leaving their familiar Visual Studio / Rider environment.
• Data analysts working in companies with .NET infrastructure and seeking an alternative to Python.
• Students and beginners wanting to understand how to apply C# for data analysis and ML.
• Team leads and architects evaluating .NET capabilities for building enterprise data solutions.

What will you learn?

• How to process large volumes of data using parallel streams and System.Threading.Tasks.Dataflow.
• How to build a prediction endpoint on ASP.NET Core in 15 minutes.
• How to use ML.NET for tasks without writing complex code — via AutoML.
• How to visualize analysis results on the web and desktop.
• How to test and monitor models in production.

The book contains dozens of ready-made examples available in a GitHub repository. All examples are written in C# 12 and .NET 8/9. You can follow them step by step, even if you have never done data science before.

"C# (.NET) for Data Science" is a bridge between classic development and modern analytics. After reading it, you will be able to independently build data processing pipelines, train models, and deploy them in real projects using the full power of the .NET platform.