Java for Data Science: From Data Collection to Machine Learning

The book "Java for Data Science" is a practical guide that will help Java developers master one of the most in-demand areas of the modern IT industry — Data Science. Unlike most Data Science books that focus on Python, this edition shows how to effectively use the Java ecosystem for collecting, processing, analyzing, and visualizing data, as well as for building machine learning models.

What is this book about?

The book covers the complete data workflow in Java: from obtaining data from various sources (databases, web APIs, CSV files, JSON) to cleaning, transforming, statistical analysis, and building predictive models. You will learn to use Java libraries for working with big data (Hadoop, Spark), visualization tools (JavaFX, JFreeChart), and machine learning frameworks (Deeplearning4j, Smile, Weka).

Who is this book for?

• Java developers who want to expand their skills and enter the Data Science field.
• Data Scientists who wish to explore an alternative technology stack in Java.
• Students and beginners interested in data analysis and machine learning.
• Team Leads and architects who need to understand how to integrate Data Science into existing Java projects.

What will you learn from this book?

• How to set up a Data Science environment in Java and work with core libraries.
• Methods for collecting and parsing data from various sources (SQL, REST API, web scraping).
• Principles of data cleaning, normalization, and transformation using Java Stream API and libraries.
• Fundamentals of statistical analysis and hypothesis testing in Java.
• Building interactive charts and dashboards for data visualization.
• Implementing machine learning algorithms (linear regression, decision trees, clustering, neural networks) using Deeplearning4j and Smile.
• Working with big data: distributed processing with Apache Spark on Java.
• Deploying machine learning models into a production environment.

The book contains numerous practical examples, ready-to-use code snippets, and real-world business cases. Each chapter is structured so you can immediately apply the knowledge in practice. The author pays special attention to the performance and scalability of solutions, which is critical for enterprise Java applications.

"Java for Data Science" is a bridge between the world of classic Java development and modern data science. If you want to remain a sought-after specialist and learn to extract value from data using Java — this book is for you.