C# (.NET) for Data Science: A Practical Guide

The world of Data Science is traditionally associated with Python and R, but the .NET platform offers powerful and convenient tools for data analysis, machine learning, and building production-ready solutions. The book "C# (.NET) for data science" is the first comprehensive guide in Russian that will show you how to use the .NET ecosystem to solve data science tasks: from data collection and cleaning to deploying machine learning models into production.

What is this book about?

The book covers the complete data lifecycle on the .NET platform:

• Data science fundamentals in C#: working with collections, LINQ for data analysis, MathNet.Numerics and Accord.NET libraries for statistical computations.
• Data processing and cleaning: loading data from CSV, JSON, SQL, Excel; methods for handling missing values, outliers, and duplicates using DataFrames (Microsoft.Data.Analysis).
• Machine learning with ML.NET: building classification, regression, clustering, and recommendation models without deep mathematical knowledge; automatic hyperparameter tuning (AutoML).
• Deep learning: integration with TensorFlow.NET and PyTorch via TorchSharp for creating neural networks.
• Data visualization: creating charts with Plotly.NET, LiveCharts2, and ScottPlot; building interactive dashboards.
• Integration with Python: calling Python code from C# via Python.NET, using NumPy, Pandas, Scikit-learn libraries in .NET projects.
• Production solutions: packaging models into microservices on ASP.NET Core, deploying in Docker and Azure ML.

Who is this book for?

• C# developers who want to enter Data Science without leaving the .NET ecosystem.
• Data Scientists familiar with Python who wish to master an alternative technology stack.
• Students and beginners studying machine learning and data analysis.
• Team Leads and architects planning to implement ML in .NET projects.

What will you learn?

• How to efficiently process large volumes of data using LINQ and DataFrames.
• How to build and train a machine learning model in C# in 15 minutes using ML.NET.
• How to visualize data and analysis results in web and desktop applications.
• How to combine the power of Python libraries with the performance of .NET.
• How to deploy a model into production and create an API for its use.

The book contains numerous practical examples, ready-to-use code snippets, and projects that will help you immediately apply the knowledge gained. Regardless of your current level, you will find valuable tools in this book for building end-to-end Data Science solutions on the .NET platform.

Note: reading the book requires only basic knowledge of C# and a general understanding of mathematical statistics. All machine learning algorithms are explained in an accessible way, with an emphasis on practice.