Java for Data Science: Data Analysis and Machine Learning in Java

The book "Java for Data Science" is a practical guide for Java developers who want to master data analysis and machine learning without leaving the Java ecosystem. Unlike popular books focused on Python, this edition shows how to harness the power of Java for building production-ready Data Science systems.

What is this book about?

The author systematically guides the reader through all stages of a Data Science project in Java:

• Data Collection: working with various sources — CSV, JSON, databases, web scraping with Jsoup, streaming data via Apache Kafka.
• Data Cleaning and Preparation: Apache Commons CSV, Tablesaw libraries, handling missing values, outliers, and normalization.
• Statistical Analysis: Apache Commons Math, computing correlations, distributions, hypothesis testing.
• Machine Learning: Smile, Deeplearning4j, Weka — from linear regression to neural networks and gradient boosting.
• Visualization: JavaFX Charts, XChart, JFreeChart — building charts and dashboards.
• Model Deployment: packaging models into REST services on Spring Boot, integration with microservices.

Who is this book for?

• Junior/Middle Java developers looking to transition into Data Science
• Team Leads choosing Java for ML components in enterprise projects
• Technical students studying Java and data analysis
• Data Scientists who want to explore the Java toolset

What will you learn?

• How to choose the right library for the task: Smile vs Deeplearning4j vs Weka
• How to process large volumes of data using Java Stream API and Apache Spark
• How to build data pipelines for automating training
• How to integrate ML models into existing Java applications
• Real-world cases: customer churn prediction, text sentiment analysis, product clustering

The book contains over 50 practical examples with full code available on GitHub. All examples are written in Java 17+ and use modern libraries. Whether you are building a recommendation system or a dashboard for business analytics — this book will become your desktop guide.

Note: The site name PythonLib should not be misleading — the book is entirely dedicated to Java, but will be useful for those seeking alternatives to Python in the world of Data Science.