This course is a practical introduction to Python for anyone interested in MLOps. It starts with the basics, such as variables, data types, conditionals, and working with lists, dictionaries, tuples, and sets. You’ll also learn about functions, how to structure them, and how to use arguments effectively.

The course gradually introduces more advanced topics like classes, object-oriented programming, and working with modules and Python scripts. It also covers how to manage your project environment using virtual environments and dependencies, which is an essential part of real-world development.

Once the foundation is set, the course moves into using Python for data handling. You’ll work with popular libraries like Pandas and NumPy to load, clean, manipulate, and analyze data. There are several hands-on lessons on exploratory data analysis, text processing in DataFrames, and visualizing data.

Toward the end of the course, you’ll apply what you’ve learned in a project based on the Titanic dataset. You’ll practice loading data, handling missing values, feature engineering, and performing analysis using Pandas. The project wraps up with writing the analysis into a Python script for easy reuse.

Finally, the course introduces you to argparse, a tool to create command-line interfaces. You’ll learn to build a simple CLI tool, giving you a small but useful taste of how Python is used in automation and scripting tasks, especially in MLOps workflows.

This course is beginner-friendly and aims to build your confidence with Python step by step.

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