vivian-okoaze/AnalystLab-Africa-Week-8-Data-Analytics-Capstone-Project

Applying the complete data analytics workflow to solve a real-world problem and communicate findings through professional documentation, visualization and presentation # Education, Technology & Economic Growth: Africa vs. Rest of the World An end-to-end data analytics project examining whether education and technology adoption are linked to economic growth, comparing Sub-Saharan Africa against the rest of the world using World Bank data (2000–2023). --- ## 📌 Project Overview Education and digital access are widely regarded as key drivers of economic development, yet the pace of progress on both fronts varies significantly across regions. This project investigates whether countries with higher levels of education and greater technology adoption experience stronger economic outcomes, with a specific focus on comparing **Africa (Sub-Saharan Africa)** against the **rest of the world**. The project follows the complete data analytics workflow — data acquisition, cleaning, modeling, analysis, visualization, and interpretation — applied to real-world World Bank data, and communicates the results through an interactive Power BI dashboard and a written report. - **Objective:** Assess the relationship between education, internet/technology adoption, and GDP per capita, and compare economic growth patterns between Africa and the rest of the world. - **Data Source:** World Bank World Development Indicators (WDI) - **Scope:** 217 countries, 9 indicators across 3 themes (Education, Technology, Economic), years 2000–2023 - **Regional grouping:** Africa (Sub-Saharan Africa, 48 countries) vs. Rest of the World (169 countries) --- ## 🛠️ Tools Used | Tool | Purpose | |---|---| | **Python (pandas)** | Data cleaning, filtering, unpivoting, joining, and validation of the raw WDI dataset | | **Google Colab** | Cloud environment used to run the Python cleaning script, chosen for its ability to handle the large raw CSV (~400,000 rows) without local performance issues | | **Google Drive** | Storage for raw and cleaned datasets, connected to Colab via `drive.mount()` for persistent access across sessions | | **Power BI Desktop** | Data modeling …