This 50-minute session introduces attendees to the fundamentals of Python for data analytics, focusing on using the ETL (Extract, Transform, Load) process to manage and analyze data efficiently. Participants will learn how to:

Ingest data: Import datasets in various formats (CSV, JSON, Excel) using pandas and numpy.

Clean data: Handle missing values, detect outliers, and apply transformations for data quality.

Shape data: Manipulate data with pandas to filter, sort, aggregate, and pivot tables for analysis.

Visualize data: Create compelling plots and graphs with matplotlib and seaborn, including 15 minutes dedicated to exploring their powerful visualization capabilities.

Save data: Export processed data into desired formats for further use or integration.

Through practical examples and a hands-on approach, attendees will see how each library supports the ETL process and gain a clear understanding of how to apply these tools to real-world data analytics challenges. By the end of the session, participants will have built a complete pipeline, from raw data ingestion to generating meaningful insights.