Hiring managers for data analyst roles are not looking for someone who finished a SQL course. They are looking for someone who has used SQL to answer a real question and can talk about what they found. These three projects are designed to give you exactly that — each one takes one to three days, uses a free public dataset, and produces something concrete to show and discuss in an interview.
Project 1 — E-commerce sales analysis (Superstore dataset)
Dataset: The Superstore sample dataset from Kaggle (search "Sample Superstore" — it is free and widely used). Load it into any SQL environment: SQLite, BigQuery free tier, or even an online SQL sandbox like db-fiddle.
The five queries to write: (1) Total revenue by product category. (2) Top 10 customers by lifetime revenue. (3) Monthly revenue trend over the full dataset period. (4) Average order value by region. (5) Products with declining sales — compare the first half of the dataset period to the second half and find the products where revenue dropped the most.
How to present it: Write up a one-page summary of your five findings as if presenting to a business stakeholder. What should the business do based on what you found? That written narrative is what makes the project portfolio-worthy.
Project 2 — Stack Overflow Developer Survey analysis
Dataset: Stack Overflow publishes their Annual Developer Survey results as a free CSV download at survey.stackoverflow.co. Use the most recent year. Load it into Python (pandas) or a SQL environment.
The questions to answer: (1) Which programming languages are most popular overall? (2) How does salary vary by years of professional experience? (3) What percentage of developers work fully remote, hybrid, or in-office — and how has that changed if you compare multiple survey years? These are real questions that come up in data analyst interviews.
How to present it: Build a simple chart for each finding and write two to three sentences interpreting what each chart shows. Interviewers will ask you what surprised you in the data — have a specific answer ready.
Project 3 — NYC taxi trip analysis (BigQuery public data)
Dataset: Google BigQuery has a public NYC taxi trips dataset available for free. Sign up for a BigQuery free account (no credit card needed for the sandbox tier) and query it directly in the console.
The questions to answer: (1) Average trip duration by time of day — are morning trips faster than evening trips? (2) Total revenue by day of the week — which day generates the most fare revenue? (3) What patterns suggest surge pricing — look at fare per mile at different times and identify when it spikes.
How to present it: This project demonstrates you can work with a very large dataset in a cloud environment — a skill that is directly relevant to most real data analyst roles. Mention the scale of the dataset in your portfolio writeup. Interviewers notice.
Three projects, each with a written findings summary, is enough to apply for entry-level data analyst roles. The goal is not perfect SQL — it is demonstrating that you can ask a business question and answer it with data.