Kotlin for Data Science: Data Analysis and Machine Learning

The book "Kotlin for Data Science" is the first practical guide in Russian that shows how to use the modern Kotlin language for solving data analysis, statistics, and machine learning tasks. Unlike the traditional Python approach, Kotlin offers strong typing, high performance, and full compatibility with the Java ecosystem, making it an excellent choice for industrial Data Science solution development.

What this book is about

The book covers the complete data workflow in Kotlin:

• Kotlin language basics for analysts — a quick start with a focus on collection processing functions, lambdas, and extensions that simplify working with data.
• Data loading and cleaning — working with CSV, JSON, Excel, databases (JDBC, Exposed). Handling missing values, outliers, and type conversions.
• Data Science libraries — a detailed breakdown of the Kotlin DataFrame, Kotlin Statistics, Kravis (visualization), and Multik (numerical computing) libraries.
• Data visualization — creating charts, histograms, heatmaps, and interactive dashboards using Kravis and Plotly Kotlin.
• Machine learning — implementing regression, classification, and clustering algorithms using Smile (Statistical Machine Intelligence & Learning Engine) and integration with TensorFlow and DL4J.
• Working with big data — data processing with Apache Spark in Kotlin, including the DataFrame API and MLlib.
• Production and deployment — packaging models into microservices (Ktor, Spring Boot), pipeline testing, and CI/CD for Data Science projects.

Who this book is for

This book will be useful for:

• Data Scientists and analysts who want to go beyond Python and use a more performant and type-safe language.
• Kotlin developers looking to master Data Science and machine learning.
• Technical students studying modern approaches to data analysis.
• Java developers seeking a more concise and expressive tool for Data Science.

What you will learn

• How to efficiently process and transform data using Kotlin.
• How to create clear visualizations for data exploration.
• How to build and evaluate machine learning models (from linear regression to neural networks).
• How to integrate Kotlin code with existing Java and Spark infrastructure.
• How to deploy Data Science solutions to production.

The book contains over 50 practical examples and exercises with ready-made solutions. All examples are available in the repository and can be run in Jupyter Notebook (Kotlin Kernel) or IntelliJ IDEA.

Buy the book "Kotlin for Data Science" on PythonLib — your step towards mastering modern data analysis tools in Kotlin.