• Fundamentals of Big Data – Importance, challenges, and application areas of large datasets.

• Data Management Framework – Strategies for storage, management, and fast analysis of large datasets.

• Data Parallelization & Scalability – Distributed processing, parallelization techniques.

• Data Preparation & Analysis – Methods for data cleaning, evaluation, and visualization.

• Application Examples

Practical Part:

• Introduction to Spark

• Hands-on practice with PySpark – Demonstrations and exercises.