This project showcases a series of SQL queries designed to analyze and cross-reference art collection data from two prominent museum databases - MoMA (Museum of Modern Art) and the Metropolitan Museum of Art. It includes operations such as extracting unique tags from artworks, aggregating artworks by these tags, and joining artist information across both collections based on ULAN (Union List of Artist Names) IDs.

This project provides a detailed analysis of the Fortune 500 companies for 2023, focusing on the distribution of female leadership across sectors, the market value of New York-based companies by industry, and identifying rapidly growing newcomers with fewer than 5,000 employees.

A comprehensive SQL analysis of an online furniture store's sales data, revealing key insights into product popularity, pricing strategies, and customer behavior. This project uncovers trends in sales prices, top-selling products by country, and sales distribution over time, offering valuable insights for optimizing sales strategies and improving customer engagement.

This project involves creating a PostgreSQL database to store information about titles available on Netflix, including movies and TV shows, and performing data retrieval queries for analytical insights.

This SQL project is tailored for analyzing the consumer electronics retail sector, focusing on sales data to uncover product performance, customer buying patterns, and effective pricing strategies. Through detailed queries, it explores monthly sales, identifies key insights into consumer preferences, and evaluates the impact of sales strategies.

This project leverages SQL for in-depth analysis of the Superstore database, focusing on product listings, pricing, and category-based financial metrics. The queries are streamlined to provide essential insights into pricing order, category value, premium items in specific categories, and inventory levels.