Welcome to the SQL Project Collections repository — a comprehensive suite of advanced SQL projects and exercises, all implemented in Jupyter Notebook. This repository is designed for learners who want to deepen their SQL knowledge and apply it to real-world scenarios, big data analysis, and complex business questions.
Maintained by MkSingh431
- About This Repository
- Advanced Topics Covered
- Project Structure
- Getting Started
- Prerequisites
- Usage Guide
- Contributing
- License
All materials in this repo are written in Jupyter Notebook, providing an interactive environment for exploring advanced SQL techniques. Leveraging both in-notebook SQL engines (like SQLite) and integrations with data analysis libraries (e.g., pandas, SQLAlchemy), these projects go beyond the basics. Here, you will encounter:
- Real-world datasets
- Industry use cases
- Complex query patterns
- Benchmarks and performance considerations
Below are some of the advanced concepts and project themes you may encounter in this collection:
- Complex Joins and Set Operations
- Multiple-table joins
- UNION, INTERSECT, and EXCEPT
- Self-joins and recursive joins
- Window Functions
- RANK(), DENSE_RANK(), ROW_NUMBER()
- LEAD(), LAG(), NTILE()
- Common Table Expressions (CTEs) and Recursive Queries
- Writing modular queries using WITH clauses
- Recursive data traversal (e.g., hierarchical data)
- Subqueries and Correlated Subqueries
- Performance Tuning and Query Optimization
- Indexing strategies
- EXPLAIN plans
- Efficient data retrieval
- Data Aggregation and Analytics
- Advanced GROUP BY queries
- Aggregates with complex CASE and FILTER usage
- Stored Procedures and User-Defined Functions (when supported by the engine)
- Data Cleaning and Transformation
- Using SQL for ETL tasks
- Handling NULLs, outliers, and inconsistent data
- Advanced Analytical Use Cases
- Cohort analysis, funnel analysis
- Time series analysis
- Integration with Python and Data Science Workflows
- Running SQL queries on dataframes
- Bringing results into Python for visualization and modeling
Each notebook provides step-by-step explanations, sample data, and detailed query walkthroughs.
Notebooks are organized by topic or use case. Folders may include:
advanced-joins/: Examples of multi-table and self-joinswindow-functions/: Analytical queries using window functionsperformance-optimization/: Indexing and tuningcase-studies/: Real-world business analyticsintegration/: Notebooks demonstrating SQL-Python interoperability
- Clone the repository:
git clone https://github.com/MkSingh431/SQL-project-collections.git
- Navigate to the directory:
cd SQL-project-collections - Open with Jupyter Notebook:
jupyter notebook
- Explore the notebooks and follow the instructions inside each project.
- Python 3.7+
- Jupyter Notebook
- SQLite or the relevant SQL engine as required by individual notebooks
- Python packages:
pandas,sqlalchemy,ipython-sql, etc.
Install common dependencies with:
pip install jupyter pandas sqlalchemy ipython-sql- Browse a topic or project folder.
- Open a
.ipynbnotebook and execute the cells step by step. - Modify SQL queries and experiment with datasets to deepen your understanding.
- For notebooks integrating with Python, run the Python code cells for visualization and further data analysis.
Contributions are highly encouraged! Add advanced notebooks, suggest improvements, or help refine existing examples. To contribute:
- Fork this repository.
- Create a new branch:
git checkout -b feature/my-advanced-topic - Add your notebook/changes.
- Submit a Pull Request.
Make sure your additions are clearly documented and use real or synthetic (non-proprietary) datasets.
This repository is licensed under the MIT License.
Level up your SQL—explore, experiment, and excel with these advanced projects!
Maintained by MkSingh431
If these resources help you, give the project a ⭐ and share it with others!