Applied Data Science with Java: Real Projects and Case Studies

Applied Data Science with Java: Real Projects and Case Studies is a comprehensive, hands-on guide that empowers Java developers, data engineers, and software professionals to build scalable, intelligent systems using modern data science tools. This book bridges the gap between traditional Java development and practical data science implementation, offering a project-based journey through real-world applications such as recommendation systems, NLP solutions, deep learning, fraud detection, and big data analytics. Built entirely within the Java ecosystem, this book leverages powerful open-source libraries like Weka, Deeplearning4j, Smile, OpenNLP, and Apache Spark. Each chapter tackles a concrete business problem—accompanied by full Java code, engineering insights, and deployment strategies using Spring Boot and Docker—guiding you from raw data to a fully operational solution. Unlike theory-heavy resources, this book emphasizes clean architecture, testable code, and production-readiness, making it ideal for professionals seeking to integrate data science into enterprise systems. From ETL workflows and feature engineering to model training, evaluation, and REST API deployment, you’ll master the full lifecycle of real-world data science applications with Java. What You'll Learn: Cleanly structure data workflows using Java libraries and patterns Train and evaluate ML models for classification, clustering, and NLP Deploy models via RESTful APIs using Spring Boot Handle streaming and big data with Apache Spark Integrate with cloud-native infrastructure using Docker Apply use cases across finance, healthcare, IoT, and e-commerce Who This Book Is For: Java developers breaking into data science Engineers integrating AI into existing applications Analysts building end-to-end data products with Java Students and professionals seeking practical, applied ML knowledge Why This Book Stands Out: Unlike most AI books that focus on Python, this book is tailored for Java professionals. It not only teaches algorithms but also equips you with architectural and engineering skills required to build reliable, maintainable, and scalable data science systems for the real world.