Kotlin for Data Science: A Practical Guide to Data Analysis
300 pages
English
PDF
4.5

Kotlin for Data Science: A Practical Guide to Data Analysis

Kotlin for Data Science: A Practical Guide to Data Analysis

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Summary:

The book "Kotlin for Data Science" is the first comprehensive guide to using the Kotlin language for data analysis, machine learning, and visualization tasks. If you are familiar with Python and want to try something new, or already use Kotlin in backend development and want to expand your skills in Data Science, this book will be your reliable guide.



The author, Alexey Voronov, a data engineer with 10 years of experience, consistently guides the reader from the basics of working with data to building complex models. The book does not require deep knowledge of Kotlin — all necessary language constructs are explained along the way.



What you will learn:

  • How to set up a Data Science environment in Kotlin (Jupyter Notebooks, Kotlin Kernel);
  • Basics of working with the Krangl (pandas equivalent) and Koma (numerical computing) libraries;
  • Methods for data cleaning, transformation, and aggregation;
  • Visualization using Kotlin Plotting and integration with Python libraries (Matplotlib, Seaborn);
  • Building machine learning models with DeepLearning4J and Smile;
  • Working with time series, text, and graphs;
  • Deploying models to production (REST API, Spring Boot).


Who this book is for:

  • Java/Kotlin developers looking to transition into Data Science;
  • Data Scientists who want to try an alternative to Python;
  • Technical students studying data analysis;
  • Anyone seeking a performant and type-safe language for working with data.


The book contains over 50 practical examples and 4 full-fledged projects: from web server log analysis to building a recommendation system. Each example is accompanied by explanations and visualizations.



Edition features:

  • Focus on idiomatic Kotlin and functional style;
  • Comparison with Python equivalents for a quick transition;
  • Ready-to-use code snippets;
  • Tips on performance optimization and working with big data.


After reading the book, you will be able to independently solve data analysis tasks of any complexity, using the full power of Kotlin — from concise syntax to full compatibility with the JVM ecosystem.

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