This project involves analyzing pizza sales data using SQL to answer key business questions and derive actionable insights. The analysis spans basic, intermediate, and advanced queries, utilizing techniques like JOINS, GROUP BY, ORDER BY, LIMIT, and window functions.
- Total Orders: Retrieved the total number of orders placed.
- Revenue Analysis: Calculated the total revenue generated from pizza sales.
- Highest-Priced Pizza: Identified the most expensive pizza on the menu.
- Most Common Pizza Size: Discovered the most frequently ordered pizza size.
- Pizza Categories: Find the total quantity of pizzas ordered in each category.
- Top 3 Pizza Types: Ranked the top 3 most ordered pizzas by revenue.
- Daily Trends: Grouped orders by date to calculate the average daily pizzas.
- Category Revenue Analysis: Identified the top 3 most ordered pizzas by revenue for each category.
- Cumulative Revenue: Analyzed the cumulative revenue generated over time.
- Joins: To combine data across multiple tables.
- SELECT: To retrieve relevant columns and calculations.
- GROUP BY: To aggregate data for analysis.
- ORDER BY: To sort data based on specific criteria.
- LIMIT: To restrict the number of results.
- Window Functions: To perform advanced analytical computations.
/queries/: Contains all SQL queries used for analysis./data/: Sample pizza sales data files (if applicable)./insights/: Summary of findings in text or visual form.
- Enhance SQL skills by solving real-world business questions.
- Utilize advanced techniques like window functions for in-depth analysis.
- Provide actionable insights to support decision-making in the pizza business.
Hi, I’m Shashank Tiwari. I’m passionate about analyzing data to uncover insights and drive business decisions. This project reflects my ability to handle real-world datasets using SQL effectively.