Data Science with Spark and Python

As data grows, traditional analytical tools often struggle to keep up with the processing demands of modern data science. Transitioning to distributed computing is essential for any professional looking to handle information at scale. This course provides a clear path from basic data manipulation to building robust, distributed pipelines using Spark and Python. You will move from local data scripts to scalable applications that can handle billions of rows. By the end of this course, you will be able to transform, analyze, and model large-scale data using industry-standard tools and techniques. What you'll learn: - Understand the core architecture of Spark and the fundamentals of distributed computing - Process large-scale datasets using DataFrames and Spark SQL for efficient analysis - Implement machine learning algorithms and recommendation systems using MLlib - Practice complex data transformations using RDDs, map, filter, and reduce operations - Apply modern data patterns including structured streaming for real-time processing - Analyze interconnected data using graph processing and GraphFrames - Configure Spark environments and optimize performance for production-ready code The course begins with essential terminology and foundational concepts before moving into practical data manipulation. You will progress through written explanations and code examples that cover everything from basic data cleaning to advanced machine learning workflows and stream processing. This course is designed for beginners in the big data space and Python users who want to scale their analytical capabilities. No prior experience with Spark or distributed systems is required. Start reading today to master the tools that power modern data science.