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.
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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
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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
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.
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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)
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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
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.
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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
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Highlight Queries:
- Top scorers by total points (games × 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
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Data Source:
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
CASEstatements
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:
📄 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.