I am conducting exploratory data visualization with the World Development Indicators dataset here. Exploratory Data Visualization involves creating multiple visualizations of the data to find interesting patterns, trends, and insights in the data. It is all about using pictures to understand the data and explore its secrets.
The task here is to create a well-defined and relevant question or hypothesis that can be explored through extensive data visualization. Throughout the project, I have utilized Python to both manipulate the data and generate visualizations. The World Development Indicators (WDI) serve as the main repository of development metrics within the World Bank, gathered from acknowledged sources. Since the dataset is quite large and contains multiple variables, I had to spend a significant amount of time in data cleaning, merging various datasets, and converting it into a format that is usable for analysis.
For more information, refer to my medium article.