SQL Data Analysis And Visualization Projects

Abstract

1. Introduction

Compilation of SQL, Tableau, PySpark data analysis related projects and challenges where I practice those skills.

2. Objective

SQL data analysis & visualization projects using MySQL, PostgreSQL, SQLite, Tableau, Apache Spark and pySpark.

This project demonstrates how Jupyter Notebook, MySQL, PostgreSQL can be applied to a real-world Data Science problem.

4. Technology Stack

• SQL Languages
• PostgreSQL
• MySQL Workbench
• DB Browser for SQLite
• Apache Spark
• Spark with pySpark
• databricks
• psycopg2

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
• pip for dependencies
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/ptyadana/SQL-Data-Analysis-and-Visualization-Projects.git
cd SQL-Data-Analysis-and-Visualization-Projects

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by ptyadana and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.