Python for Data Engineering Course

In this training program you will learn how to extract data from multiple sources, clean and transform large datasets, automate ETL workflows and work with databases using Python. By the end of this training, you will be able to build reliable data pipelines, handle real-world data challenges and support analytics and big data systems with confidence.

Prerequisites:

• Basic Python programming
• Core data structures & control flow
• Functions
• Basic file handling
• Introductory SQL
• Basic understanding of relational databases

Who Should Take This Course:

• Aspiring Data Engineers
• Software Developers
• Software Engineers
• IT Professionals (especially those working with data, databases, or pipelines)
• Data Professionals & Analysts

What Will You Learn:

• Python for data engineering
• Data ingestion from files, databases, and APIs
• Data cleaning and transformation with Pandas and NumPy
• Working with SQL databases using Python
• Building ETL and ELT pipelines
• Data modeling and warehousing concepts
• Big data processing with PySpark
• Workflow orchestration basics (Airflow concepts)
• Cloud-based data handling fundamentals
• Writing efficient, reusable, and testable Python code

What You Will Be Able to Do After Completing This Training:

• Write Python programs to process and transform large datasets
• Build and manage end-to-end data pipelines
• Extract data from APIs, files, and databases
• Clean, validate, and prepare data for analytics and reporting
• Work confidently with SQL databases using Python
• Use Pandas and NumPy for efficient data manipulation
• Process big data workloads using PySpark
• Apply ETL and ELT concepts in real-world projects
• Follow data engineering best practices for reusable and reliable code
• Prepare for entry-level to mid-level data engineering roles