PORTFOLIO

SQL & DATA ANALYSIS

Extracting Insights Through Relational Logic and Structured Queries

Behind every analysis lies a relational structure—tables, keys, and queries that connect data points into a meaningful whole.

The network-like imagery symbolizes the logic of SQL: joins, relationships, data modeling, and efficient querying that allows us to extract exactly the information we need.

In this section, I present projects based on SQL analysis, database exploration, and query optimization to answer real analytical questions.

Turning relational data into structured insights through logic and query design.

Relational databases store complex business information across interconnected tables. These projects focus on exploring, querying, and analyzing structured data using SQL to extract insights that reflect real-world business scenarios. From revenue breakdowns to healthcare databases and sales trend analysis, each project applies analytical thinking and business logic to transform raw tables into meaningful results.

Using PostgreSQL, MySQL, and SQLite, I design optimized queries that leverage joins, aggregate functions, subqueries, and window functions. These projects go beyond syntax, emphasizing how SQL supports decision-making through cohort analysis, funnel metrics, retention tracking, and reporting workflows connected to realistic datasets.

Related Projects

E-commerce Revenue & Funnel Metrics

This project aims to analyze revenue and funnel metrics in an e-commerce environment using the public Olist dataset The analysis is designed to answer key business questions.

Healthcare Database Analysis

This project analyzes a dataset of U.S. hospitals to extract operational and quality insights: emergency coverage, distribution by state/ownership/type, and patterns in the overall rating (1–5).

Sales & Inventory Trends Using Date Functions

This repo builds a small data mart in Postgres from the Online Retail (UK) dataset and simulates inventory to analyze sales, stockouts, and Days of Hand (DoH).