Data Cleaning and Processing Fundamentals for Analytics

Raw data is rarely ready for analysis, often containing errors, duplicates, and inconsistencies that can lead to incorrect business insights. This course teaches you how to identify, clean, and transform messy datasets into structured, reliable information. You will start with the fundamental concepts of data integrity and tidy data principles. Then, you will progress to practical text-based exercises where you will learn to handle missing values, correct formatting discrepancies, and merge data sources using modern data processing techniques. What you'll learn: - Understand the core principles of data integrity, quality, and tidy data structures - Identify and resolve common data anomalies, duplicates, and missing values - Clean and format text, dates, and numerical values using standardized patterns - Merge, join, and reshape datasets from multiple sources safely - Apply basic data validation techniques to ensure accuracy before analysis - Practice data transformation workflows through structured written exercises This course begins with essential terminology and foundational data concepts before moving into practical cleaning strategies. It is designed entirely for beginners with no prior data preparation experience. Start building your foundation in data preparation and ensure your analyses are built on clean, reliable data.