Data Preparation: Structuring Inputs for Visualization

Effective data visualization starts long before you write the first line of plotting code. Learn how to transform messy, real-world data into the clean, structured format modern graphics tools expect. By the end of this course, you will understand the fundamental principles of the data layer, allowing you to confidently prepare datasets for any visualization tool based on the grammar of graphics philosophy. What you'll learn: Understand the role of the data layer within the layered grammar of graphics framework. Master techniques for reshaping data, including pivoting, melting, and aggregation, to suit different visualization types. Apply essential data cleaning practices, such as handling missing values, outliers, and inconsistent data types. Practice transforming raw data into tidy data principles (one observation per row, one variable per column). Configure data schemas and types using modern typing methods to ensure robust and error-free plotting inputs. This course begins with core terminology and concepts related to data structure and type, then progresses through practical steps for cleaning and reshaping datasets. You will apply these written techniques through targeted practice exercises. This course is designed for absolute beginners in data analysis or anyone who struggles with data preparation prior to plotting. No prior experience with programming or visualization tools is required. Start building your visualizations on a strong, reliable data foundation today.