Handling Missing Data in Python with pandas

Real-world datasets are rarely perfect, and dealing with incomplete information is one of the most common challenges in data science. This course provides a clear, step-by-step path to managing missing values effectively so your analyses and machine learning models remain accurate and reliable. You will transition from feeling overwhelmed by messy data to confidently preparing clean, production-ready datasets. What you'll learn: - Identify and locate missing values across different data types in pandas - Apply modern imputation techniques to fill gaps without introducing bias - Drop incomplete records safely using precise filtering criteria - Use pandas nullable data types to handle missing integers and booleans correctly - Analyze the patterns of missingness to understand why data is absent - Implement robust data cleaning workflows that integrate with modern Python pipelines This course begins with foundational concepts, defining the types of missing data and how Python represents them. You will then progress through practical, written exercises that demonstrate how to clean, fill, and transform your datasets using industry-standard pandas functions. This course is designed for beginners and intermediate data enthusiasts who want to build a solid foundation in data cleaning; basic familiarity with Python syntax is helpful but no advanced prior knowledge is required. Start writing cleaner, more reliable data preparation code today.