Data Cleaning Foundations with SQL and Spreadsheets

Raw data is almost always messy, incomplete, or inconsistent, making it highly unreliable for analysis. To make accurate, data-driven decisions, you must first learn how to transform "dirty" data into a clean, structured format. This text-based course guides you through the foundational principles of data cleaning and preparation. You will learn how to detect anomalies, handle missing values, and standardize datasets using industry-standard tools like spreadsheets and SQL, ensuring your data is always ready for analysis. What you'll learn: Understand the core principles of data integrity and the impact of poor data quality; Clean and format datasets efficiently using spreadsheet functions and formulas; Write SQL queries to filter, transform, and standardize database records; Handle missing values, duplicates, and inconsistent data types systematically; Apply modern data validation techniques to prevent errors before analysis begins; Document your cleaning process to ensure reproducibility and transparency. We begin with essential terminology and the theory of data quality, then progress through step-by-step written explanations demonstrating practical cleaning techniques in both spreadsheets and SQL. This course is designed for aspiring data analysts and beginners with no prior data cleaning experience. Start reading today and gain the practical skills needed to work with real-world datasets.