10 SQL Project Ideas

SQL projects typically require you to a) create a database system or b) write SQL queries to analyze an existing dataset. A project or SQL take-home challenge provides hands-on experience in SQL database design and data analytics. You will gain real-world experience querying databases, which can help you learn SQL faster.

The most common types of SQL projects are exploratory data analysis projects. In an EDA project, you’ll use SQL to query a dataset to draw out interesting insights. Typically, you’ll then package those insights in a visualization.

Other SQL projects include database creation, SQL web apps, or SQL take-home challenges. You’ll find inspiration for your next SQL project in this guide, which consists of 10 projects to try and free datasets to use.

This Nextdoor take-home challenge tests your ability to use SQL for EDA and build a KPI dashboard. First, you’re asked to query the social media dataset to answer the following:

• How many users are there?
• In which month did the most users join?
• Who has the most followers?

Next, you’re asked to develop a KPI dashboard, specifically the tables that will serve the dashboard. This requires intermediate-to-advanced knowledge of database design. Finally, you’re asked to write the queries that populate the dashboard.

How you can do it: Look at the Nextdoor take-home on Interview Query. You’ll find available data there. Additionally, you can create your project using a similar format with a different social media dataset.

This SQL project requires multiple steps. First, you’ll create a series of databases and tables to manage and organize a library’s collection of books. Then, you’ll develop a system that allows users to check out books and search books by category.

This is similar to any library system, as there are numerous data points to track and manage, including author name, date published, category, call numbers, date checked out, etc. So this project helps with database design and scaling to accommodate a vast dataset.

How you can do it: You can see the source code for a library SQL project to help visualize the finished product. Or you can make the project your own and develop a similar system for book listings on Goodreads or the San Francisco Public Library dataset.

When you’re given a data analytics take-home, often it will look like this example from Uber. You’re given a subset of Uber’s table schema in it, and you’re then required to provide a single query to answer various questions about the dataset.

These are advanced SQL questions that require multi-part queries to execute. Here’s a sample question from the Uber take-home:

“For each city, calculate the 90th percentile difference between Actual and Predicted ETA for all completed trips within the last 30 days.”

In addition, the take-home includes a data analytics portion, and you’re asked to use SQL to query the data and determine factors that can predict whether a sign-up will start to drive.

How you can do it: Access the Uber SQL take-home on Interview Query. The take-home includes Uber table schema and additional analytics and experiment metrics design questions.

Data analysts at e-commerce businesses are regularly called on to develop inventory management systems. This helps the company perform inventory planning, as well as keep track of and visualize the status of its inventory.

You can use numerous e-commerce datasets to build inventory management and prediction systems. Typically you’ll need sales data as well as inventory data.

Then, you can create a retail database in SQL that will help answer questions like, “Which months have the highest sales?” or “Which product categories are most affected by seasonality?”

How you can do it: You can use the Walmart sales dataset. The company ran a Kaggle challenge previously that asked users to help build a system to predict inventory needs. Another helpful dataset is the Ajax e-commerce sales and inventory dataset.

This SQL take-home from Smule provides a short test of your query writing skills. This is good practice for writing complex SQL statements that generate specific metrics.

With this assignment, you’re provided with three tables and asked to answer a series of questions like:

• How many users are installed?
• What proportion came through paid channels?

This is a good SQL project for anyone interested in marketing analytics or growth marketing roles. In total, there are seven questions to answer, and these range from simple SQL queries to advanced subqueries with joins.

How you can do it: Look at the Smule take-home challenge on Interview Query. You’ll also find some probability and abstract brain teaser-type questions, which is good practice for a data science interview.

SQL is an excellent tool for querying sports analytics datasets to draw out interesting insights like:

• Factors that can affect the outcome of a match
• Highest-scoring players
• Winningest teams
  
• Team scoring percentage

This project lets you dive into soccer statistics. You can use the European Soccer Database. The dataset features data points for more than 25,000 matches, 10,000 players, and nearly six years of games.

How you can do it: Follow this Kaggle notebook for data analysis of the soccer data in SQL. You can also jump into baseball or football data. Ultimately, you’ll want to package the insights you find in a visualization.

This is another data science take-home challenge in SQL. The challenge provides table schema and asks you to answer questions like “What percentage of patient visits are still admitted or not discharged yet from the hospital?”

You can also explore healthcare analytics metrics like length of stay (LOS), analyze LOS, and build a model to evaluate these metrics. The challenge asks you to provide code and written documentation.

How you can do it: See the Qvestus take-home on Interview Query. This challenge takes about six hours to complete and test SQL and data storytelling.

This is a beginner SQL project that simulates a student registration portal. Essentially, the portal will gather student input and then insert those details into a database. This is a good practice for anyone interested in banking roles, healthcare, or eCommerce.

In addition, this project works by storing passwords in an encrypted form and creating a unique student ID generated after the user registers.

How you can do it: See this GitHub repository for example code. For more practice, try the Student Enrollment SQL Challenge, which includes questions (with solutions) for querying a student enrollment database.

If you are relatively new with analytics, supermarket data systems offer an ideal introduction. These systems provide a rich landscape brimming with data analytics project opportunities. Supermarket analytics offer a straightforward yet expansive setting for testing your proficiency with modern data platforms.

In this project, we delve into an optimal method for honing your SQL skills: the analytics sandbox. Through the Supermarket Sales SQL Takehome Challenge, you’ll engage with a dynamic data sandbox, complemented by a step-by-step progress checker, ensuring your inaugural data analytics project is both enlightening and successful.

This is another EDA project in SQL. Essentially, you’ll use open datasets from the UK Department of Transportation to analyze road safety and fatality statistics. You can use this data to answer questions like:

• Assess the severity of motorcycle accidents
• Calculate Average Severity by vehicle
• Find the total number of motorcycle accidents

There’s a lot of exploratory analysis you can perform with the dataset and create your own questions and comparisons. Does seasonality affect road accidents in the UK? What month had the most fatalities?

How you can do it: Explore this GitHub repository, which features links to the data and sample SQL queries that answer some of the questions above.

Take the Interview Query SQL course to build your SQL expertise. This course covers the basics, all the way to complex joins, aggregations for analytical reporting, and creating multi-table databases.