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Projects/Ecommerce Sales Analysis with SQL
beginner5 milestones · ~6 hours

Ecommerce Sales Analysis
with SQL

Your first real data analytics project. You'll be handed credentials to a live ecommerce database and three open-ended business questions. Your deliverable is a public GitHub repository: SQL files, written findings, and a portfolio-ready README. Single toolchain: VS Code + SQLTools + Git. No prescribed JOIN paths, no answer keys — just real analytical work with feedback at each checkpoint.

What you'll learn

  • —Approach an unfamiliar database the way a working analyst does
  • —Translate vague business questions into concrete, performant SQL
  • —Communicate analytical findings to non-technical stakeholders in Markdown
  • —Leverage AI responsibly to sharpen executive narratives while authoring 100% of your SQL
  • —Ship a polished GitHub repo as your first portfolio piece
  • —Get used to receiving and acting on review feedback

After this project

What you'll be able to claim — credibly — once your repo is shipped.

Roles you can credibly apply to

Junior Data Analyst (entry-level)
Writing analytical SQL against a real database is the first competency hiring managers test for in entry-level analyst roles.
— This is a free first project — pair with one paid project (RFM, dashboard, or churn) to demonstrate breadth.
Operations / Business Analyst
Operations roles need someone who can pull a number from a database and explain it to a non-technical reader.
— Add a spreadsheet-heavy project (campaign performance) to round out the toolkit ops teams use daily.
Reporting Analyst
Revenue, AOV, customer concentration — the bread-and-butter metrics this project covers — are the same ones reporting analysts publish weekly.
— A BI-tool project (executive dashboard) makes you competitive for reporting roles at any company > 50 people.

Keywords on your resume after this

Tools
PostgreSQLSQLVS CodeSQLToolsGitGitHub
Methods
CTEsJOINsGROUP BYWindow functionsAggregationsTime-bucketed analysis
Concepts
Revenue analysisAOV (Average Order Value)Customer concentrationRepeat-purchase behaviorBusiness KPIs

Interview questions you'll be ready for

  1. 01Walk me through how you'd analyze monthly revenue trends in a database you've never seen before.
  2. 02What's AOV, and what changes when you compute it monthly vs all-time?
  3. 03How do you decide whether to include cancelled or refunded orders in a revenue calculation?
  4. 04If 20% of customers drove 80% of revenue, what would you recommend the business do differently?
  5. 05Walk me through a query that finds the top 10 customers by lifetime spend.
Resume-ready bullets — unlocked when you reach Milestone 5
After completing the project, you'll get a copy-ready resume bullet template phrased the way hiring managers want to hear it, with the numbers your own analysis produced.

The 5 milestones

Each milestone is reviewed before you advance.

  1. 01

    Set up your project repo

    Before any analytical work: create a public GitHub repository, push the standard skeleton, and paste the URL into your workspace.

  2. 02

    Get the lay of the land

    You've just joined an ecommerce company as their first data analyst. Your CTO wants to know what's actually in the database.

  3. 03

    How healthy is this business?

    The CTO read your one-pager. Now they need a board-ready story on revenue, AOV, and growth.

  4. 04

    Who are our customers and how do they shop?

    Marketing wants segments. You define them, with evidence.

  5. 05

    Pull it together for your portfolio

    Curate three milestones into one coherent story a recruiter can read in five minutes.

Reading & references

Pre-flight setup: editor, database, Git, GitHub
Setup Guide
How to read an unfamiliar database
Article
Aggregations and time-bucketed reporting in Postgres
Article
Ecommerce KPIs every analyst should know
Article
Window functions for customer-level analysis
Article
Anatomy of a great portfolio README
Article

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