Java for Data Science: From Beginner to Data Analysis Professional

The book "Java for Data Science" is a comprehensive guide that reveals the power of the Java language in the field of data analysis and machine learning. Contrary to the common belief that Data Science is exclusively the domain of Python, the author clearly demonstrates that Java, with its performance, scalability, and rich ecosystem of libraries, is an ideal tool for building industrial solutions in data processing.

The book is structured on the principle of "from simple to complex." Starting with the basics of statistics and probability necessary for any data professional, you will quickly move on to practical tasks. Each chapter contains not only theory but also ready-to-use code examples in Java that can be immediately applied in real projects.

What you will learn from this book:

• Data Science Fundamentals in Java: environment setup, working with ND4J, Deeplearning4j, Smile, and Apache Commons Math libraries.
• Data Collection and Cleaning: web scraping, working with CSV, JSON, databases, and streaming processing of large volumes of information.
• Machine Learning: implementing algorithms for linear and logistic regression, decision trees, random forest, SVM, and gradient boosting.
• Deep Learning: building neural networks with Deeplearning4j, working with CNNs for images and RNNs for time series.
• Natural Language Processing (NLP): tokenization, stemming, text sentiment analysis, building chatbots in Java.
• Data Visualization: creating charts, histograms, and dashboards using JavaFX and the XChart library.
• Production and Scaling: deploying models in microservices, integration with Hadoop and Spark, performance optimization.

Who this book is for:

• Java developers looking to master a new specialization and transition into the field of Data Science.
• Technical students studying machine learning and data analysis.
• Data Scientists who want to use Java to build high-load systems.
• Team Leads and architects choosing a technology stack for analytical projects.

Unlike many books that only superficially touch on the topic, this edition offers deep immersion with real-world cases: from forecasting financial time series to building recommendation systems. You will learn not just to run ready-made libraries, but also to understand the mathematical principles underlying the algorithms, which will allow you to adapt them to your unique tasks.

Bonus for readers: at the end of the book, you will find a chapter with a complete project — creating a real-time customer review analysis system that combines all the acquired skills. The source code for all examples is available for download.

The book "Java for Data Science" will become your reliable companion on the path from a simple programmer to a sought-after specialist capable of solving complex data analysis problems in one of the most powerful programming languages in the world.