Mastering Big Data with PySpark Course Course

Module 1: Introduction to the Course

⏳ 30 minutes

• Topics: Course orientation; PySpark within the big data landscape

• Hands-on: Set up your Educative environment and explore the sample dataset

Module 2: Introduction to Big Data

⏳ 1 hour 15 minutes

• Topics: Big data concepts, processing frameworks, storage architectures, ingestion strategies

• Hands-on: Complete the “Introduction to Data Ingestion” quiz and review solutions

Module 3: Exploring PySpark Core and RDDs

⏳ 1 hour 15 minutes

• Topics: Spark architecture, resilient distributed datasets, RDD transformations and actions

• Hands-on: Write and execute RDD operations on sample data; pass the RDD quiz

Module 4: PySpark DataFrames and SQL

⏳ 1 hour 30 minutes

• Topics: DataFrame API, Spark SQL operations, data exploration and advanced manipulations

• Hands-on: Perform DataFrame transformations and complete the Data Structures quiz

Module 5: Customer Churn Analysis Using PySpark

⏳ 45 minutes

• Topics: End-to-end churn analysis workflow: preprocessing, feature engineering, EDA

• Hands-on: Work through the “Customer Churn Analysis” case study and quiz

Module 6: Machine Learning with PySpark

⏳ 1 hour 30 minutes

• Topics: ML fundamentals, PySpark MLlib overview, pipeline construction, feature techniques

• Hands-on: Build a simple ML pipeline and pass the MLlib quiz

Module 7: Modeling with PySpark MLlib

⏳ 1 hour 15 minutes

• Topics: Regression, classification, unsupervised learning, model selection, evaluation metrics

• Hands-on: Train and evaluate models; tune hyperparameters in provided exercises

Module 8: Predicting Diabetes in Patients Using PySpark MLlib

⏳ 45 minutes

• Topics: Diabetes prediction case study: data prep, model build, evaluation

• Hands-on: Complete the “Predicting Diabetes” quiz and solution walkthrough

Module 9: Performance Optimization in PySpark

⏳ 1 hour 15 minutes

• Topics: Partition optimization, broadcast variables, accumulators, DataFrame performance tips

• Hands-on: Optimize sample queries and pass the Performance Optimization quiz

Module 10: PySpark Optimization: Analyzing NYC Restaurants Data

⏳ 45 minutes

• Topics: Real-world optimization on NYC dataset; best practices for efficient queries

• Hands-on: Apply optimization techniques and review solution code

Module 11: Integrating PySpark with Other Big Data Tools

⏳ 1 hour

• Topics: Connecting PySpark with Hive, Kafka, Hadoop, and integration best practices

• Hands-on: Configure and test integrations; complete the integration quiz

Module 12: Wrap Up

⏳ 15 minutes

• Topics: Course summary, key takeaways, next steps in big data learning

• Hands-on: Reflect with the final conclusion exercise and project challenge

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