Create Your Own SQL Portfolio Project
Turn your SQL skills into a meaningful project that showcases your interests and abilities.
Project Guide
Overview
You've learned beginner, intermediate and advanced SQL skills. Now it's time to build a project you care about.
In this level, we're not handing you a dataset. Instead, you'll learn how to:
- Identify a meaningful business question
- Choose an industry you're excited about
- Source your own dataset
- Design and analyze it with SQL
This is your portfolio — make it reflect your interests, your strengths, and your dream job.
Learning Objectives
By the end of this project, you will:
- Learn how to brainstorm a data project from scratch
- Choose an industry that aligns with your job goals
- Identify the kinds of data that matter in that industry
- Find a relevant dataset (or combination of datasets)
- Design and run an analysis that mirrors a real business problem
Step-by-Step Brainstorm Guide
1. Pick Your Industry
Think about:
- What kinds of companies you want to work for
- What types of products or tools you enjoy using
- Where you already have domain knowledge
2. Ask: "What Do Businesses in This Industry Care About?"
Examples:
- E-commerce → What products are driving the most profit?
- Healthcare → How long does it take to schedule an appointment?
- SaaS → Are new users actually activating key features?
3. Find a Dataset That Could Help You Answer It
Use platforms like:
4. Design Your Database
- Which tables will you need?
- What are the key columns?
- Are there any relationships (e.g. users & transactions)?
Sketch out your schema using tools like draw.io or dbdiagram.io
5. Write Your Questions
Start with 3–5 questions that could be answered with SQL:
- Which marketing channels had the highest conversion?
- What's the average delivery delay by region?
- Which customer segment has the highest repeat purchase rate?
Example Brainstorms
Example 1: E-commerce
Scenario: You want to work in a consumer tech or retail company like Shopify or Amazon.
Business Goal: Identify which products are most profitable and how customer behavior affects sales.
Possible Questions:
- Which products have the highest return rate?
- Which product categories see the most seasonal demand?
- Do customers who use coupons spend more or less over time?
Possible Dataset: Online Retail Dataset (UCI / Kaggle)
Example 2: Healthcare
Scenario: You're interested in data roles in hospitals, clinics, or health tech.
Business Goal: Understand delays and inefficiencies in patient scheduling.
Possible Questions:
- What's the average time between booking and appointment?
- Which departments have the longest patient wait times?
- Are cancellations correlated with time of day or day of the week?
Possible Dataset: Hospital Appointment No-Show Dataset (Kaggle)
Example 3: SaaS / Productivity Tools
Scenario: You're targeting companies like Notion, Asana, or Airtable.
Business Goal: Explore user engagement patterns over time.
Possible Questions:
- What percentage of users activate at least one key feature?
- How does activation rate differ by plan tier?
- What's the weekly retention for new users?
Possible Dataset: Simulated SaaS Customer Engagement Data
What You'll Build
There's no right answer. But by the end of this, you should walk away with:
- A dataset you found and loaded yourself
- A SQL file with 5+ exploratory or analytical queries
- A short summary of the business insights you uncovered
Finally, turn this into a polished case study or share it publicly on your portfolio.
Get started with our FREE Notion portfolio templateReflection Prompt
After completing your project, take 10 minutes to reflect:
What would you do differently next time?
- Would you pick a different dataset?
- Were your questions too easy or too hard?
- Did you struggle with any particular SQL techniques?
- What would you do next if this were a real company problem?
Write your reflection in a short paragraph and keep it with your project files. It will help you grow faster and speak more clearly in interviews.
Whenever you're ready, move on to the 4th and final stage of your SQL learning journey -- Data Engineering fundamentals.