This repository contains a collection of basic SQL queries I've written to practice and reinforce my understanding of fundamental SQL concepts. It's a personal learning project, perfect for beginners or anyone who wants a quick refresher on core SQL skills.

A simulated academic database designed for mastering SQL queries across a variety of real-world school scenarios—students, instructors, courses, grades, and scheduling.

• Focus Areas:

  • Database design and table creation with foreign keys
  • Joins across multiple tables (Students, Enrollments, Courses, Instructors)
  • Aggregation and filtering by semester, year, and grade
  • Practical views like class rosters, teaching assignments, and academic stats

• Highlight Queries:

  • Student-course-instructor relationships via joins
  • Enrollment stats by course, semester, and grade
  • Sorting and filtering by enrollment date, email domain, and credit load
  • Determine which instructors teach specific courses
  • Count of courses taught per instructor and students per course

📄 View SchoolDB README

A simulated eCommerce database focused on customers, orders, products, and order details. This project explores various business logic scenarios like customer activity, revenue calculations, and order trends.

• Focus Areas:

  • Filtering, pattern matching, and sorting
  • Aggregations and grouping (SUM, AVG, COUNT)
  • Joins across multiple related tables
  • Revenue analysis, product performance, and customer behavior
  • Subqueries and integrity validation (e.g., mismatched totals)

• Highlight Queries:

  • Calculate total and average revenue per customer
  • Identify top 3 spending customers
  • Find all products never ordered
  • Validate order totals by comparing aggregated order detail prices
  • Retrieve detailed product purchase reports per customer and order

📄 View EcommerceDB README

A deep-dive into WNBA draft data with a focus on player performance, draft trends, and team-level insights. This project enhances SQL proficiency through ranking functions, conditional logic, and aggregate analysis.

• Focus Areas:

  • Filtering and conditional queries (e.g., player stats, draft years)
  • Aggregation, grouping, and ranking (RANK() OVER, GROUP BY)
  • Advanced joins and subqueries for year-over-year comparisons
  • CASE statements for custom labels like 'Veteran' vs. 'Rookie'
  • Win shares analysis and college/team breakdowns

• Highlight Queries:

  • Top scorers by total points (games ×ばつ points per game)
  • Players ranked by win shares per 40 minutes
  • Average points per college and draftees per team
  • Label players as veterans or rookies based on years played
  • Determine draft-year leaders by performance

• Data Source:

  • WNBA Draft Basketball Player Data (1997–2021) on Kaggle

📄 View WNBADraftDB README

A comprehensive SQL analysis of Grammy Awards data from 1965 to 2024, focused on uncovering insights into artist performance, award categories, and nomination patterns. This project reinforces key SQL concepts using a real-world entertainment dataset.

Focus Areas:

• Filtering and pattern matching (e.g., song titles with "Love")
• Aggregation and grouping (wins vs. nominations per artist/category)
• CTEs and subqueries for advanced summarization
• Identifying duplicates, outliers, and unique records
• Conditional logic using CASE statements

Highlight Queries:

• Most nominated artist in a single year
• Artists with Grammy wins in 3+ different categories
• Duplicate nominations across years and categories
• Songs with multiple producers or recurring title themes
• Artists who were nominated but never won

Data Source:

• Grammy Winners and Nominees (1965–2024) on Kaggle

📄 View GrammysDB README

• SQL Server (T-SQL)
• Custom-built sample databases
• SSMS (SQL Server Management Studio)

• Practice SQL querying techniques in real-world-style datasets.
• Reinforce foundational and intermediate SQL concepts.
• Build a portfolio of practical SQL solutions.