Data Cleaning and Scatter Plot Visualization for Economic Indicators

Raw economic data is often messy, fragmented, and difficult to interpret without the right analytical approach. Understanding how to prepare this data and present it visually is a core skill for any aspiring analyst or researcher. This text-based course guides you through the foundational concepts of data preparation and visualization, showing you how to transform raw country-level indicators into clear, insight-driven scatter plots. You will start with key terminology, basic data structures, and foundational definitions before moving into practical data manipulation. Through clear written explanations and structured code examples, you will learn how to clean messy datasets, merge disparate sources, and build polished visualizations that reveal the relationship between economic freedom and national wealth. What you'll learn: - Understand foundational data structures and visualization principles for economic datasets - Clean raw country-level data by handling missing values and formatting inconsistencies - Merge separate datasets containing GDP and economic freedom indicators using modern data libraries - Create readable scatter plots with proper labels, scales, and trend indicators - Interpret visual patterns and correlations between economic policies and wealth outcomes - Apply modern data formatting conventions to ensure your analytical workflow is reproducible This course begins with basic data concepts and moves step-by-step through the process of cleaning, merging, and plotting. It is designed entirely for beginners, requiring no prior experience in data science or programming. Start reading today to build your data visualization skills.