Kotlin for Data Science: Data Analysis and Machine Learning

The book "Kotlin for Data Science" is a practical guide for developers and analysts who want to master modern approaches to working with data using the Kotlin language. Unlike traditional textbooks focused on Python or R, this publication fills a gap in the documentation for using Kotlin in the field of Data Science, offering ready-made solutions and proven methodologies.

Who is this book for?

• Java/Kotlin developers transitioning to the field of data analysis
• Data Scientists looking to try an alternative to Python with stricter typing
• Technical students studying modern programming languages
• Engineers working with big data and the JVM ecosystem

What you will learn from the book:

• Basics of working with DataFrame libraries (Kotlin DataFrame, Krangl) and their differences from pandas
• How to perform data preprocessing, cleaning, and transformation using Kotlin
• Data visualization using Lets-Plot and other Kotlin-compatible libraries
• Building machine learning models using Smile, DL4J, and integration with TensorFlow
• Working with Big Data: Apache Spark and Kotlin for distributed computing
• Integrating Kotlin code with Jupyter Notebook and Zeppelin for interactive analytics
• Performance optimization and working with multithreading in data tasks

Each chapter contains practical examples with real datasets, from simple statistical tests to creating custom machine learning pipelines. The book is structured on the principle of "from simple to complex": starting with basic work with CSV files, you will move on to building neural networks, leveraging the advantages of Kotlin's static typing and null-safety.

Special attention is paid to performance: you will learn how Kotlin can be faster than Python in typical data tasks, and how to use its strengths (coroutines, inline functions, optimized collections) to speed up data processing. In the appendix, you will find syntax cheat sheets and a comparison of equivalent Kotlin and Python constructs.

The book will be useful both for beginners in Data Science and for experienced professionals seeking alternative tools. It will teach you not just to use ready-made libraries, but also to understand how data analysis works at the JVM level, allowing you to create efficient and reliable solutions for production environments.