🌍 World Database SQL Analysis

📌 Project Overview

In this project, I explored the World database, a commonly used dataset for practicing SQL queries. This dataset provides information on countries, cities, and populations, making it a great resource for data analysts to sharpen their MySQL skills.

🧐 Why Use the World Database?

The World database is essential for data analysts and SQL learners because it:

• Offers real-world data on countries, cities, and population statistics.
• Allows practice with key SQL concepts such as JOINs, filtering, ordering, and aggregation.
• Helps in building data-driven insights into global demographic trends.

📊 SQL Queries & Insights

Below are some of the SQL queries I worked on, along with their insights:

🔎 1. Retrieve the Capital City of Spain

SELECT city.name
FROM city
JOIN country ON city.id = country.capital
WHERE country.name = 'Spain';

Insight: This query finds the capital city of Spain by joining the city and country tables using the capital field.

📌 Result:

---

🌍 2. List All Capital Cities in Europe (Ordered Alphabetically)

SELECT city.name
FROM city
JOIN country ON city.id = country.capital
WHERE continent = 'Europe'
ORDER BY name;

Insight: This query provides a sorted list of European capital cities, making it easier to analyze geographical distributions.

📌 Result:

---

📈 3. Find the Average Population Per Country

SELECT name, AVG(population) AS 'Average'
FROM country
GROUP BY name
ORDER BY AVG(population) DESC;

Insight: This query calculates the average population per country and sorts them in descending order, highlighting the most populous countries.

📌 Result:

---

🚀 How to Use This Project

If you'd like to explore this dataset and SQL queries yourself, follow these steps:

1. Download the World database from MySQL Sample Databases.
2. Import it into your MySQL environment.
3. Run the provided SQL queries to analyze the data.
4. Modify and expand queries to uncover more insights!

🏆 Key Takeaways

• Learned how to join tables to extract meaningful relationships.
• Practiced filtering, sorting, and aggregating data.
• Developed insights into population trends and geographic distributions.

📢 Feel free to fork this repository and try out the queries yourself. Let's keep learning and growing in data analysis! 🚀