Data Cleaning with Pandas and NumPy

Raw data is rarely ready for analysis, often arriving with missing values, inconsistent formatting, and duplicate records. To make accurate data-driven decisions, you must first master the art of data cleaning. This text-based course provides a structured framework to transform messy datasets into pristine, analysis-ready resources using Python's most powerful data libraries. You will begin with foundational concepts, learning how to identify common data quality issues and structure your cleaning workflow. Step-by-step, you will explore how to manipulate dataframes, handle anomalies, and apply robust cleaning techniques to real-world scenarios. What you'll learn: - Understand the core principles of data quality and structured cleaning workflows - Clean and normalize string data, dates, and numeric columns using Pandas - Handle missing, null, or duplicate values systematically with NumPy and Pandas - Reshape and reindex dataframes to establish consistent structures for analysis - Apply modern Python type hinting to ensure your data manipulation functions are robust and maintainable - Validate cleaned data to guarantee accuracy and consistency before modeling This course starts with fundamental definitions and essential terminology before guiding you through practical, written exercises and code-based scenarios. You will progress from basic data manipulation to building a reusable, structured cleaning pipeline. This course is designed for beginners, aspiring data analysts, and Python enthusiasts who want to build a solid foundation in data preparation. No advanced mathematical or programming background is required. Start reading today to turn chaotic datasets into reliable, structured insights.