Data Preparation and Analysis: Clean and Structure Raw Data

Raw data is rarely ready for immediate analysis, often containing missing values, duplicates, and inconsistent formatting. Understanding how to clean and structure this data is the most critical step in any data professional's workflow. This text-based course guides you through the foundational concepts and practical techniques required to turn messy datasets into reliable assets for analysis. You will learn how to identify data quality issues, apply modern transformation workflows, and prepare datasets for robust exploratory analysis. What you'll learn: Understand core data preparation concepts, terminology, and tidy data principles; Identify and resolve common data quality issues like missing values, duplicates, and outliers; Apply modern dataframe techniques to filter, group, and aggregate structured data; Format and transform diverse data types, including dates, strings, and categorical variables; Structure clean datasets optimized for downstream analytical tools and reporting. The course begins with essential terminology and data quality fundamentals before moving into step-by-step written explanations of data manipulation, cleaning strategies, and modern analytical workflows. Designed entirely for beginners, this course requires no prior programming or advanced mathematics experience. Start reading today and build a solid foundation in modern data preparation.