Project-Based Learning

Real-World Data Analysis Projects

Tackle end-to-end analytical problem statements across Retail, Marketing, Education, and Food Science with dual Python & SQL solutions.

E-Commerce Sales Growth & Revenue Decomposition

Retail & Commerce

Difficulty: Beginner

Open in Data Analysis Lab

📋 Business Problem Statement

A retail brand wants to evaluate monthly revenue trajectory, identify peak sales volume periods, and calculate average order values across Q1 and Q2.

🎯 Project Analytical Goal

Identify the top-performing months, compute month-over-month revenue growth rate, and prepare a boardroom-ready executive summary.

Executable in Pyodide WASM

# Python Solution: Calculate MoM Growth
df['MoM_Growth_Pct'] = df['Revenue'].pct_change() * 100
df['Avg_Price_Per_Unit'] = df['Revenue'] / df['Units_Sold']

print("=== Sales Growth Analysis ===")
print(df[['Month', 'Revenue', 'Units_Sold', 'MoM_Growth_Pct', 'Avg_Price_Per_Unit']])

Key Findings & Business Takeaways

Recommended Chart: Grouped Bar Chart (Month vs Revenue) and Line Graph (Units Sold trend).

June delivered peak revenue at ₹29,00,000, driven by a 17.9% surge in units sold.
Average price per unit remained remarkably stable between ₹2,666 and ₹2,950 across all 6 months.
Q2 total revenue exceeded Q1 by 47.9%, demonstrating strong seasonal acceleration.