Data Cleaning Fundamentals with Spreadsheets and SQL

Raw data is rarely ready for analysis, often containing duplicates, formatting errors, and missing values that can lead to incorrect business decisions. Learning how to systematically clean and validate this data is one of the most critical skills for any aspiring data professional. In this course, you will transition from handling messy, disorganized datasets to producing high-quality, analysis-ready data. You will master the fundamental techniques of data profiling, cleaning, and verification using industry-standard tools like spreadsheets and SQL, while also learning how to document your processes for transparency and reproducibility. What you'll learn: - Understand the foundational principles of data integrity, quality metrics, and the cost of dirty data. - Apply spreadsheet formulas and functions to standardize text, remove duplicates, and handle null values. - Write SQL queries to filter, transform, and clean large datasets stored in relational databases. - Implement modern data validation techniques to verify the accuracy and completeness of your cleaned data. - Document and report your data cleaning workflow to ensure transparency and reproducibility. - Practice data profiling to identify structural errors and anomalies before starting the cleaning process. The course begins with core definitions of data quality before guiding you through hands-on written explanations and code-based exercises in both spreadsheets and SQL. You will progress from simple formatting fixes to database cleaning operations and verification workflows. This course is designed for absolute beginners looking to build essential data preparation skills, with no prior data cleaning experience required. Start reading today to turn chaotic datasets into reliable, structured insights.