SQL for Analytics
Your project is not just a set of smart queries. It is a small evidence pack.
A reviewer should be able to open your repo, load the sample data, run your SQL, compare outputs, and understand what the numbers mean.
Use one public GitHub repository. Keep it small and easy to inspect on a phone or laptop.
Your repo should show the full path from product question to SQL answer. Do not hide the data checks or only show final screenshots.
0 of 8 lessons done · proved by a submitted hackathon project
Do each one yourself, then tap it to tick it off. The ticks are only a checklist for you: they are not marked or scored.
0 of 5 done
This layout is enough for a hackathon submission. Change names to match your project, like food delivery, UPI expense tracking, edtech lessons, or campus events.
product-sql-analytics/
README.md
data/
users.csv
events.csv
orders.csv
queries/
01_check_counts.sql
02_weekly_active_users.sql
03_signup_to_purchase_funnel.sql
04_signup_cohorts.sql
05_top_products.sql
outputs/
01_check_counts.csv
02_weekly_active_users.csv
03_signup_to_purchase_funnel.csv
04_signup_cohorts.csv
05_top_products.csvWrite the README for a busy reviewer. They may spend only two minutes before deciding if your work is clear.
# Product SQL Analytics Project
## Context
This project analyses sample events for a campus food ordering app.
The goal is to understand activation, ordering, and repeat use.
## Questions answered
1. How many users are active each week?
2. What percent of signups place their first order?
3. Which signup cohorts return in later weeks?
4. Which items generate the most revenue?
## How to run
1. Create tables using the CSV files in /data.
2. Run queries in the order shown in /queries.
3. Compare your results with the files in /outputs.
## Checks done
- Checked row counts for each table.
- Checked date ranges before using them.
- Checked that funnel steps do not increase after each stage.
- Checked that revenue equals quantity * price.
## Key insights
- Weekly active users increased from 420 to 610 in the sample period.
- 38% of signups placed an order within 7 days.
- The August signup cohort had better week 2 retention than July.
## Next action
Improve first-order conversion by testing a small discount on day 1.A common mistake is to submit only final charts or one long SQL file. That makes your work hard to verify.
Another mistake is using AI-generated insights without checking the numbers. If ChatGPT or Gemini helps you write the README, check every figure against your output files.
You can ask an AI tool to review your README for clarity or turn your rough notes into cleaner insights.
Do not ask it to invent business meaning. Paste your output table and ask: “Summarise only what this table supports. Mention any limitation.” Then verify the wording.
Do each one yourself, then tap it to tick it off. The ticks are only a checklist for you: they are not marked or scored.
0 of 5 done
Answer the quick check to finish this lesson.