Data Cleaning with Pandas: University Towns Dataset

Raw data is rarely ready for analysis, and cleaning it is one of the most critical skills for any data professional. This text-based course guides you through the practical process of taking a messy dataset of university towns, loading it into Python, and transforming it into a structured, clean format. You will learn how to identify inconsistencies, handle missing values, and apply modern Pandas workflows to make your data reliable. By the end of this course, you will be able to confidently import, inspect, and clean raw CSV files using Python. What you'll learn: - Understand foundational data cleaning concepts and common data entry errors. - Load raw CSV data into Pandas DataFrames and run initial diagnostic checks. - Clean and format string data using modern Python string methods and regular expressions. - Handle missing, duplicate, or malformed values systematically. - Apply type hints and modern Pandas practices to ensure your data pipeline is robust. This course begins with essential definitions and foundational concepts before moving into step-by-step data cleaning techniques. It is designed for beginners who have a basic understanding of Python and want to build practical data manipulation skills. Start learning today and master the art of data cleaning.