This repository showcases a collection of SQL-based projects demonstrating expertise as a Data Analyst. The projects exemplify how SQL can be effectively used for data extraction, cleaning, analysis, and deriving actionable business insights across various domains. Each project is meticulously crafted to highlight real-world problem-solving skills and analytical rigor.

Key Features of the Portfolio:

1. Comprehensive Data Analysis: Projects focus on solving practical business problems such as: Warehouse Optimization: Analyzing storage utilization and inventory distribution. Queries are designed to explore datasets and uncover meaningful insights.

2. Data Cleaning and Validation: Strong emphasis on ensuring data quality by addressing common issues such as: Duplicate records. Missing or inconsistent entries. Cross-table dependency mismatches. SQL techniques like DISTINCT, GROUP BY, and HAVING are used to clean and validate data effectively.

3. Advanced SQL Techniques: Use of: Subqueries: For layered data exploration. Joins: To connect and analyze multiple related datasets. Aggregate Functions: For summarizing and deriving metrics such as averages, totals, and counts. Demonstrates the work of relational databases and perform deep dives into complex datasets.

4. Performance Optimization: Queries are structured for efficiency, ensuring scalability for large datasets. Techniques like indexing and partitioning are utilized when needed.

5. Business Impact: Each project provides actionable insights to drive strategic decisions.

6. Professional Documentation: Each project includes: Objective: A clear definition of the business problem being addressed. SQL Methodology: Step-by-step breakdown of the approach. Insights: Interpretation of results and their implications for business decisions.

7. Skills Demonstrated: SQL Proficiency: Comprehensive use of SQL for data manipulation, aggregation, and visualization preparation. Data Storytelling: Transforming raw data into actionable business insights. Problem-Solving: Addressing operational inefficiencies, enhancing customer engagement, and optimizing workflows. Attention to Detail: Ensuring data integrity and consistency across datasets.

8. As a Data Analyst I can: Work with diverse datasets to address unique business challenges. Ensure high data quality for reliable decision-making. Provide meaningful insights that drive operational improvements and strategic planning.

📂 Projects Included

• COVID-19 Data Exploration: Trend analysis, joins, and KPIs on infection data.
• Nashville Housing Data Cleaning: Advanced cleaning using CTEs, CASE WHEN, and normalization.
• Mint Classics Sales Analysis: Sales KPIs, profit margins, and inventory optimization queries.