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https://github.com/mouad-el-asri/analyze_international_debt_statistics

In this project I have used my data engineering skills to write SQL queries to answer interesting questions about international debt using data from The World Bank. 📊
https://github.com/mouad-el-asri/analyze_international_debt_statistics

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In this project I have used my data engineering skills to write SQL queries to answer interesting questions about international debt using data from The World Bank. 📊

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  International Debt Analysis Project




# Analyze_International_Debt_Statistics 🌍💰



Welcome to the **International Debt Analysis Project**! Here, I took a deep dive into the world of international debt statistics provided by The World Bank. Through crafting SQL queries, I unearthed valuable insights and discovered answers to intriguing questions surrounding the debt owed by developing countries. Get prepared to join me in the journey of exploring, analyzing, and interpreting this critical financial data.



## Project Description 🌐



Countries, like individuals, sometimes rely on debt to manage their economies and facilitate growth. The World Bank plays a pivotal role in providing financial support to developing countries in the form of debt. One example of this is investing in infrastructure, a costly but essential aspect of improving citizens' quality of life.



In this project, I delved into a comprehensive dataset containing information about the debt amount (in USD) owed by various developing countries. My task was to address intriguing questions such as:



1. What is the total debt owed by the countries in the dataset?

2. Which country holds the largest debt, and what is the corresponding amount?

3. What is the average debt owed across different debt indicators?



## Project Tasks 📊



1. **The World Bank's International Debt Data**: I familiarized myself with the dataset containing international debt information sourced from The World Bank.



2. **Finding the Number of Distinct Countries**: I utilized SQL queries to determine the total number of distinct countries present in the dataset.



3. **Finding Out the Distinct Debt Indicators**: Through SQL queries, I identified the unique debt indicators within the dataset.



4. **Totaling the Amount of Debt Owed by Countries**: I wrote SQL queries to calculate the total debt owed by the countries included in the dataset.



5. **Country with the Highest Debt**: I crafted SQL queries to pinpoint the country with the highest debt and determine the exact debt amount.



6. **Average Amount of Debt Across Indicators**: I developed SQL queries to calculate the average debt amount owed by countries across various debt indicators.



7. **The Highest Amount of Principal Repayments**: I utilized SQL queries to uncover the highest principal repayment amount.



8. **The Most Common Debt Indicator**: By employing SQL queries, I identified the most frequently occurring debt indicator in the dataset.



9. **Other Viable Debt Issues and Conclusion**: I reflected on additional noteworthy debt-related insights and concluded the project with a summary that ties everything together.



Get ready to uncover valuable insights into international debt trends and patterns, contributing to a deeper understanding of the global economic landscape.



## Dataset Source 📈



The dataset used in this project is provided by The World Bank. It encompasses both national and regional debt statistics for various developing countries across the years 1970 to 2015.





  
Crafted with ❤️ by Mouad El Asri