Data Analysis - Dominos Store

This project-based course introduces SQL techniques commonly used by analysts to explore, clean, and analyze pizza sales and customer data. Working with the p1_dominos_db dataset, learners will perform practical, hands-on analysis to understand order patterns, revenue, customer behavior, and menu performance. The findings and queries developed in this course are intended to support data-driven decision-making for a retail food business such as Domino’s.

What you'll learn

• How to explore and understand a real-world relational dataset (p1_dominos_db).
• Data cleaning and preparation techniques using SQL (handling nulls, deduplication, type conversions).
• Querying patterns and aggregations to analyze order frequency, peak times, and revenue drivers.
• Customer behavior analysis: repeat customers, average order value, and retention signals.
• Menu and item-level performance analysis to identify best- and worst-selling products.
• Basic reporting queries and design of summary tables to support business decisions.

Core Topics & Sample Activities

• Intro to the dataset schema and relationships (orders, customers, menu items, stores).
• Exploratory queries: counts, DISTINCT, GROUP BY, ORDER BY, window functions.
• Filtering and joins: INNER, LEFT, and common join patterns for combining sales & customer data.
• Aggregation & time-series analysis: daily/weekly/monthly revenue and order trends.
• Customer segmentation using SQL: high-value vs occasional customers.
• Creating reusable views and summary tables for reporting dashboards.

Who this course is for

Analysts, data-curious professionals, or students with basic familiarity with SQL who want a practical, business-focused project demonstrating how to extract insights from sales and customer data. Ideal for those preparing to work on retail, F&B, or e-commerce datasets.

Prerequisites

• Basic understanding of SQL (SELECT, WHERE, JOIN, GROUP BY).
• Access to a SQL environment that can host or connect to the p1_dominos_db dataset.

Course Outcome

By the end of this project, learners will have a collection of well-documented SQL queries and summary tables that describe order patterns, revenue trends, customer behaviors, and menu performance for Domino’s-style store data. These artifacts will be directly usable to inform business decisions and to build dashboards or further analyses.