Build and evaluate regression models in PySpark using linear, GLM, and ensemble methods.

Apply logistic regression, decision trees, and Random Forests for classification.

Implement K-Means clustering and assess scalable ML workflows with PySpark.

Apply RFM analysis and K-Means clustering for customer segmentation.

Extract and analyze textual data using OCR with PySpark DataFrames.

Build and interpret Monte Carlo simulations for uncertainty modeling.

Apply Scala fundamentals including variables, functions, and advanced concepts.

Implement Spark RDD operations, streaming, and fault-tolerant pipelines.

Build real-time big data solutions integrating Spark with external systems.

Install and configure PySpark, Hadoop, and MySQL for ETL workflows.

Build Spark applications for full and incremental data loads via JDBC.

Apply transformations, handle deployment issues, and optimize ETL pipelines.

Describe Spark architecture, core components, and RDD programming constructs.

Apply transformations, persistence, and handle multiple file formats in Spark.

Develop scalable workflows and evaluate Spark applications for optimization.