Applied Data Science Using PySpark

Applied PySpark for Real Data Science Workflows

Applied Data Science Using PySpark focuses on the full predictive model-building cycle, not just isolated Spark examples. The book begins with environment setup and PySpark fundamentals, then moves steadily into feature selection, supervised and unsupervised learning, validation, and the practical business of turning models into something you can run and maintain.

What makes this title especially useful is its end-to-end orientation. Instead of stopping at theory, it connects data preparation, modeling, evaluation, automated pipelines, and deployment so readers can see how the pieces fit together in a real workflow. The result is a hands-on guide for anyone working with big data who wants to use PySpark as a serious data science tool.

What the Book Covers

• PySpark setup and environment configuration
• Core data manipulation with RDDs and DataFrames
• Utility functions, visualizations, and feature engineering support
• Variable selection methods, including PCA and model-based approaches
• Supervised and unsupervised learning techniques
• Model evaluation, cross-validation, and time-series validation
• MLflow, automated pipelines, and model deployment

Built for Practitioners

The structure makes this a strong match for data scientists, machine learning engineers, and analytics professionals who already know they need more than notebook experiments. Its emphasis on reusable workflows, operationalization, and deployment gives the book a distinctly practical edge, while the PySpark focus keeps it squarely in modern distributed-data territory. 📊

Why It Belongs in a Digital Library

If you are looking for a PySpark ebook that connects modeling methods with implementation details, this is a focused and well-scoped choice. It reads like a working manual for people who need to build, validate, and ship models with Spark rather than simply study the concepts from a distance.