Java for Data Science: From Basics to Industrial Solutions

The book «Java for data science» is a practical guide that bridges the gap between classic Java development and modern data analysis tasks. Unlike Python, Java has long remained in the shadow of data science, but with the growing demands for performance, scalability, and integration into enterprise systems, Java is becoming an indispensable tool for building industrial ML solutions. This book is created for those who want to learn data science in Java from scratch or systematize their existing knowledge.

Who is this book for?

• Java developers looking to transition into data analytics and machine learning
• Data Scientists who want to use Java for production systems
• Students and teachers studying applied statistics and ML
• Engineers working with big data (Hadoop, Spark) who need analytical tools

What will you learn from the book?

• Basics of data science in Java: working with arrays, matrices, statistical functions (Apache Commons Math, ND4J)
• Libraries for data analysis: deep dive into Smile, Tribuo, Weka, Deeplearning4j
• Machine learning: linear regression, decision trees, random forest, SVM, neural networks
• Natural Language Processing (NLP): tokenization, stemming, sentiment analysis with OpenNLP and Stanford NLP
• Data visualization: creating charts with JFreeChart and JavaFX Charts
• Integration with Big Data: how to use Java ML models in Spark and Hadoop
• Model deployment: REST API on Spring Boot, Docker, monitoring in production

Each chapter contains ready-made code examples that can be run immediately and adapted to your tasks. The author focuses on code performance and readability, which is especially important for industrial development. You will learn not just to run algorithms, but to understand how they work «under the hood», how to choose hyperparameters, and how to interpret results.

This book will become your desktop reference for data science in Java — from simple statistical calculations to full-fledged machine learning pipelines. Regardless of whether you are solving a text classification problem, time series forecasting, or building a recommendation system, you will find proven recipes and best practices here.

Start your journey into data science with Java today — this book will give you all the necessary tools and confidence in your abilities.