Mastering Big Data with PySpark Course Syllabus

Overview: This course provides a hands-on, text-based journey into PySpark and big data engineering, designed by MAANG engineers. Over approximately 10 hours of content, you'll progress from foundational concepts to real-world applications, mastering distributed data processing, machine learning pipelines, and performance optimization. Each module combines theory with interactive exercises and quizzes, culminating in practical case studies that solidify your skills in scalable data workflows.

Module 1: Introduction to the Course

Estimated time: 0.5 hours

• Course orientation
• PySpark within the big data landscape
• Setting up the Educative environment
• Exploring the sample dataset

Module 2: Introduction to Big Data

Estimated time: 1.25 hours

• Big data concepts and characteristics
• Processing frameworks overview
• Storage architectures and ingestion strategies
• Completing the Introduction to Data Ingestion quiz

Module 3: Exploring PySpark Core and RDDs

Estimated time: 1.25 hours

• Spark architecture fundamentals
• Resilient Distributed Datasets (RDDs)
• RDD transformations and actions
• Executing RDD operations on sample data

Module 4: PySpark DataFrames and SQL

Estimated time: 1.5 hours

• DataFrame API basics and advantages
• Spark SQL operations
• Data exploration techniques
• Advanced DataFrame manipulations

Module 5: Machine Learning with PySpark

Estimated time: 3 hours

• ML fundamentals and PySpark MLlib overview
• Pipeline construction in PySpark
• Feature engineering and transformation techniques
• Model training, evaluation, and hyperparameter tuning
• Case studies: Customer churn and diabetes prediction

Module 6: Performance Optimization in PySpark

Estimated time: 1.75 hours

• Partition optimization strategies
• Broadcast variables and accumulators
• Efficient DataFrame operations
• Real-world optimization using NYC restaurants data

Module 7: Integrating PySpark with Other Big Data Tools

Estimated time: 1 hours

• Connecting PySpark with Hive
• Integration with Kafka and Hadoop
• Best practices for end-to-end workflows

Prerequisites

• Basic knowledge of Python programming
• Familiarity with data analysis concepts
• Understanding of command-line interfaces

What You'll Be Able to Do After

• Design and execute distributed data processing workflows using PySpark
• Apply machine learning pipelines to real-world datasets with MLlib
• Optimize Spark jobs for performance using partitioning and broadcast techniques
• Integrate PySpark with Hive, Kafka, and Hadoop for end-to-end solutions
• Solve business problems like customer churn and disease prediction using scalable tools