SQL Data Analytics Project

A comprehensive data analytics project with data exploration, analysis and reporting. It is divided into 2 parts:

1. Exploratory Data Analysis - EDA (scripts 1-6)
2. Advanced Analytics (scripts 7-13).

The project is performed in PostgreSQL, adapted from a MS SQL Server course.
Designed as a portfolio project which highlights industry best practices in data analytics.

📊 Project Overview

Part 1: Exploratory Data Analysis (EDA)

Performed an initial investigation of the dataset to summarize its main characteristics and identify patterns:

• Database Exploration
• Dimensions Exploration
• Date Exploration
• Measures Exploration
• Ranking Analysis (Top N / Bottom N).

Key Techniques Used: JOINS, UNION ALL, GROUP BY, ORDER BY, LIMIT, Aggregate Functions and Window Functions (DENSE_RANK()).

Part 2: Advanced Analytics

Conducted a deeper analysis to solve complex business problems and derive actionable insights:

• Change Over Time
• Cumulative Analysis
• Performance Analysis
• Part-to-Whole Analysis
• Data Segmentation
• Reporting.

Key Techniques Used: CTEs, Subqueries, Window Functions (LAG(), AVG() OVER()), CASE statements, and creating 2 analytical Views for simplified reporting.

🛠 Tech Stack

• DBMS: PostgreSQL
• IDE: DBeaver
• Key Concepts: Advanced SQL Analytics, Query Optimization

Credits

This project was developed as part of the "SQL Course" taught by Baraa Khatib Salkini (MIT Licensed).

About Me

I'm Burliyat Osmanova, a Data Analyst with a foundation in Economics and practical experience in building analytical solutions for a medical organization. I am passionate about uncovering data-driven insights and leveraging technical skills like SQL, Python, and BI tools to solve business problems. This project is part of my hands-on learning journey.

Contact Me

• LinkedIn: Burliyat Osmanova
• Telegram: @osmanovabm
• Email: osmanovabm@gmail.com