Fall 2023 - Introduction to Structured Database Environments - Analyzing SQL Databases

Course: Introduction to Structured Database Environments
Tools Used: MySQL, SQL querying

Project Overview
In this project, I was tasked with analyzing return data for a fictional company using a structured SQL database called Quantigration RMA. The goal was to extract, join, and aggregate information across multiple relational tables (Orders, RMA, and Customers) to support the product manager in identifying return trends by state and product type.

Key Objectives:
Identify the number of product returns by state

Analyze return frequency by product SKU

Calculate return percentages by product type

Communicate insights and limitations for stakeholder decision-making

Using SQL queries, I uncovered that Massachusetts had the highest number of product returns, and the Enterprise Switch 10GigE SFP+ 48 port (SKU: ENT-48-10F) was one of the most frequently returned items. Further analysis revealed that over 22% of returns were associated with a single SKU — BAS-48-1 C, indicating a potential product quality issue.

What I Learned:
How to write and optimize SQL JOIN and GROUP BY queries across multiple tables

The importance of data cleaning and structure in relational databases

How to turn raw SQL outputs into clear summaries for non-technical stakeholders

That context matters — while the data showed high return rates, it lacked detail on why returns occurred, emphasizing the need for better data design

Reflection:
This was one of my first projects working directly with a real-world style SQL database. It helped me build confidence in querying and analytical thinking, and it highlighted how critical thoughtful data structure and reporting are when supporting business decisions. This assignment laid the groundwork for deeper, more refined data projects I would take on later in my degree.