Kotlin Data Science. Data Science with Kotlin

Introduction

The book «Kotlin Data Science» (author: David Katzman, 2020, 320 pages) is the first comprehensive guide dedicated to applying the Kotlin language in the field of data science. While Python and R remain the dominant tools for data analysis, Kotlin offers unique advantages: static typing, concise syntax, full compatibility with Java, and excellent performance. The author sets an ambitious goal — to show that Kotlin can be not just an alternative, but a full-fledged platform for building end-to-end data pipelines: from collection and cleaning to visualization and deployment of machine learning models.

What This Book Is About

The book is divided into three logical parts, each covering key stages of working with data. The first part introduces the basics of Kotlin for data science: setting up the environment, working with collections, functional programming, and integration with Jupyter Notebooks via the Kotlin Kernel library. The second part is dedicated to libraries for data analysis: Kotlin DataFrame (an analog of pandas), Kravis (visualization in the style of ggplot2), and KMath (numerical computing). The third part is the most practical: it covers machine learning using KotlinDL (deep learning) and Smile (statistical models), as well as model deployment via REST API and integration with cloud services.

Who This Book Is For

The book is aimed at a broad audience:

• Kotlin/JVM developers who want to expand their skills into data science.
• Data scientists familiar with Python/R but wanting to try a more performant and type-safe language.
• Students and teachers looking for a modern textbook on data analysis on an alternative platform.
• Machine learning engineers who need to integrate models into existing Kotlin/Java projects.

What the Reader Will Learn

• How to set up a data science environment in Kotlin (IntelliJ IDEA, Jupyter Notebooks, Gradle).
• Basics of working with the Kotlin DataFrame library: loading, filtering, grouping, and aggregating data.
• Data visualization using Kravis: creating charts, customizing styles, and exporting.
• Numerical methods and linear algebra with KMath.
• Building machine learning models: from linear regression to neural networks with KotlinDL.
• Model quality evaluation, cross-validation, and hyperparameter tuning.
• Deploying models to production: creating a REST API with Ktor, working with databases and cloud services.
• Practical examples: time series analysis, text processing, working with images.

Strengths of the Book

The first and main advantage is relevance. At the time of its release (2020), it was the only book systematically describing data science in Kotlin. The second is practical focus: each section contains ready-to-run code examples. The third is clear structure: from simple to complex, with gradual immersion into details. Particularly noteworthy is the chapter on visualization: the author not only shows the Kravis syntax but also explains the principles of effective data visualization (choosing chart types, working with color, legends). The book also contains numerous links to documentation and additional resources, making it a good starting point for self-study.

Who It Is Recommended For

I recommend this book to anyone who already has a basic knowledge of Kotlin and wants to apply it in data analysis. Beginners in data science will find it useful to start with this book if they are willing to simultaneously learn the basics of statistics and machine learning. Experienced Python