Welcome to this comprehensive data analytics project designed for beginners and advanced users alike. Embark on a journey to delve into the economic and social landscapes of G20 countries using World Bank economic and social data. Leverage the wbdata web API protocols to seamlessly collect data from the World Bank website, master data transformation and cleaning with pandas, and apply various data analysis techniques to uncover valuable insights.
Before embarking on this data analytics expedition, ensure you possess the following prerequisites:
Python Programming: A solid foundation in Python programming is essential to navigate the project's codebase.
Pandas Library: Familiarity with the pandas library, a cornerstone of data manipulation and analysis in Python, is crucial for effective data handling.
Data Visualization Libraries: Acquaint yourself with data visualization libraries like matplotlib or seaborn to present your findings in a visually compelling manner.
Git Version Control: Basic knowledge of Git, the ubiquitous version control system, is recommended to track your project's progress and maintain a clean development history.
This project comes equipped with a rich collection of materials to guide you through your data analytics journey:
Data Collection Script: A meticulously crafted Python script to effortlessly gather data from the World Bank website using the wbdata API.
Data Cleaning and Transformation Notebook: A Jupyter notebook meticulously designed to clean and transform the collected data, preparing it for insightful analysis.
Data Analysis Notebook: Embark on a voyage of discovery with this Jupyter notebook, where you will apply a variety of data analysis techniques to extract valuable insights from the data.
Visualization Notebook: Transform your data into captivating visualizations using this Jupyter notebook, effectively communicating your findings to a wider audience.