Data is everywhere, but before you can analyze it, you need to load it efficiently and correctly. "Data Ingestion with Pandas" is designed to help you master the art of importing data from various sources, including flat files, Excel, databases, APIs, and JSON sources, using Python’s powerful Pandas library.

This course takes you through every aspect of data ingestion, from reading simple CSV's and Excel files to handling large datasets efficiently. You’ll learn how to work with Excel files, including reading multiple sheets, selecting specific columns, and handling large files. As we progress, you'll dive into working with databases, where you'll connect to Relational databases, connect to them and retrieve data using queries, and seamlessly integrate it into Pandas.

In the modern data landscape, APIs and JSON are crucial sources of real-time data. This course will equip you with the skills to extract data from web APIs, parse complex JSON structures, and transform them into clean, structured DataFrames. Along the way, you’ll also learn how to handle common ingestion errors, such as encoding issues, missing values, and memory limitations, ensuring a smooth and efficient data-loading process.

Whether you’re a data analyst, data scientist, or software engineer, mastering data ingestion is an essential skill. This course is very practical, hands-on, and designed for both beginners and those looking to refine their Pandas expertise.

By the end, you’ll have the confidence to import and manage data from multiple sources efficiently, setting the stage for deeper analysis and machine learning workflows. Let’s dive in and unlock the power of data ingestion with Pandas.