Optimizing Databases with SQL

The primary objective of this project is to analyze sales data related to different chocolate products, sales personnel, and geographical locations to derive meaningful insights and answer specific queries using SQL. This project involves querying data from multiple tables related to chocolate sales to answer business-related questions. The analysis covers data retrieval, filtering, aggregation, and conditional logic using SQL queries. The project uses multiple tables stored in a database named Chocolate Project. The tables include sales, people, geo and products.

The project involves the use of various SQL techniques, including:

SELECT statements for data retrieval.

JOIN operations to combine data from multiple tables.

Aggregation functions such as COUNT and SUM.

Conditional statements for filtering data.

Grouping results using GROUP BY.

Ordering results using ORDER BY.

Key Queries and Insights:

High-Value, Low-Volume Shipments: identified shipments where the amount exceeds $2,000 but fewer than 100 boxes were sold.

Sales Performance in January 2022: Counted the number of shipments each salesperson handled in January 2022.

Product Popularity: Milk Bars vs. Eclairs: Compared the total boxes sold of Milk Bars and Eclairs.

Sales in Early February 2022: Analyzed sales of Milk Bars and Eclairs during the first seven days of February 2022.

Active Salespersons in Early January 2022: Listed salespersons with at least one sale in the first seven days of January 2022.

Inactive Salespersons in Early January 2022: Identified salespersons who did not make any shipments in the same period.

High-Volume Shipments: Counted instances where more than 1,000 boxes were shipped monthly.

Shipping ‘After Nines’ to New Zealand: Verified if at least one box of 'After Nines' was shipped to New Zealand every month.

Monthly Sales Comparison: India vs. Australia: Compare the number of chocolate boxes sold monthly in India and Australia.

The project successfully utilized SQL queries to analyze and derive insights from the chocolate sales data. Key findings include identifying high-value shipments, comparing product sales, determining sales performance by salespersons, and evaluating geographical sales distribution.

Through the Chocolate Sales Analysis project, I have significantly enhanced my SQL skills, particularly in the areas of data retrieval, filtering, and aggregation. I learned to effectively use JOIN operations to combine data from multiple tables, enabling comprehensive analysis of interconnected datasets. The project taught me to utilize conditional statements and aggregation functions such as COUNT and SUM to extract specific insights from the data. I also gained experience in identifying and analyzing high-value, low-volume shipments, comparing product sales, and evaluating sales performance across different time periods and geographical locations. Additionally, I developed the ability to translate business questions into precise SQL queries, allowing for actionable insights and informed decision-making. This project has reinforced my understanding of SQL as a powerful tool for data analysis and its practical applications in real-world business scenarios