Author: Chang Jia Jun, Date: 5/22/2026
This is a walkthrough of data science fundamentals using SQL and Python, focusing on clarity and simplicity.
Analyze a fictional dataset of bike retail store orders with data using Python and SQL to uncover business insights, identify trends, and provide actionable recommendations.
The fictional bike store is a relational database designed for teaching SQL and analytics. The dataset models typical retail entities (brands, categories, products, customers, orders, order_items, stocks, staffs, and stores) and is provided as CSV files in the input\bike-store-sample-database folder.
This project demonstrates common data analysis workflows: extracting and joining relational data, cleaning and transforming CSVs, exploratory data analysis with Python, simple SQL analytics (sales by product/store, customer segmentation), and visualizations for business insights. The notebook includes reproducible steps to calculate KPIs (revenue, average order value, top-selling products) and time-based trends.
Contents:
notebook.ipynb— main analysis notebook.requirements.txt— Python package dependencies.input\bike-store-sample-database— dataset CSV files.
References: