I Created a Harry Potter Dataset…

and it’s pretty magical.

I didn’t build this just for fun, I grounded it in real analyst experience by designing it after tables I’ve used in the real world.

Customers, products, campaigns, sales, suppliers, and transactions are the everyday things an analyst might pull for an ad hoc report, campaign ROI analysis, or segmentation tests. And these are all of the things you can find in my dataset.

In the world of data, beginners are taught coding and analytical skills using datasets like “AdventureWorks” (for Microsoft SQL Server) or the “Titanic Survival Dataset” (for Python).

Here’s a table I made that shows how my dataset stacks up against the classics.

Rethinking Analyst Learning Resources

Data analytics is the driving force behind innovation. Yet while most resources focus on teaching analysts coding and technical skills, very few focus on innovating the resources themselves. At the same time, more people than ever are entering this field, eager to learn SQL, Python, and the tools that power today’s businesses.

💡Confession Time: I failed my first Python programming class. Even to this day, Python still scares me — and I’m still learning new things. It takes persistence, practice, and patience to get comfortable with writing code, debugging errors, and thinking like a programmer.

With that in mind, it’s time we start rethinking the kinds of practice datasets and learning materials we give analysts. These are the future professionals who will one day drive the very innovation that companies value and emphasize in their core values today. If learning resources are stale, repetitive, or disconnected from real-world scenarios, we’re holding them back before they even start.

We also talk so much about storytelling in analytics — but storytelling doesn’t have to start only at the presentation stage. What better way to help analysts learn than through a dataset where they can create stories while they query? By practicing in a world like the Harry Potter broomstick store, analysts can bring imaginative thinking into their training.

That means when they step into the real world, they’ll walk in with more than just technical skills, they’ll also have creativity skills. And those two together are what turn raw data into insights that inspire stakeholders and drive real impact.

That’s why I built this dataset: to prove that resources can be both realistic and imaginative, grounding analysts in real business logic while sparking the kind of storytelling and creativity that will carry through their careers.

And for me, this is a fulfilling beginning to a journey that I’m happy I finally decided to take.

Check out the full dataset here:

Mercury’s Broomstick Emporium

what’s in the dataset

🧙 customers.csv

• Columns: customer_id, first_name, last_name, date_of_birth, house, occupation, year
• Includes both students and adults. Occupations range from “Student” to roles like Auror, Healer, or Ministry of Magic employee. Adults are identified as those born before 01/01/2008.

🛒 sales.csv

• Columns: sale_id, date, payment type , campaign_id, customer_id
• Each record is a transaction linked to a customer and (optionally) a campaign.

📦 sale_details.csv

• Columns: detail_id, sale_id, product_id, quantity, galleons_spent
• Line-item details that make the dataset realistic for SQL joins and sales analysis.

🛍️ products.csv

• Columns: product_id, product_name, speed, control, price, supplier_id
• Brooms, Quidditch wear, potions, and wizard supplements—everything a witch or wizard might buy.

🏪 suppliers.csv

• Columns: supplier_id, supplier_name, reliability, lead_time
• Features Nimbus Racing Broom Co., Cleansweep Broom Co., and more wizarding suppliers.

🎯 campaigns.csv

• Columns: campaign_id, name, start_date, end_date, spend
• Hogwarts-style promotions and discounts—back-to-school specials, Quidditch sales, holiday bundles.

🎯 campaigns_products_with_id.csv

• Columns: campaign_product_id, campaign_id, product_id
• Bridge table connecting campaigns to multiple products (many-to-many).

LEARN TO DO A MARKET BASKET ANALYSIS IN PYTHON

If you’re interested in exploring the dataset and want to learn something new, I’ve created two notebooks on Kaggle that walk you through a market basket analysis.

Lesson 1: Explore here.

Lesson 2: Explore here.

Each notebook contains detailed steps and explains the “why” behind everything – see below. I wrote this with the intention that it can aid analysts and non-analysts (like product managers, business managers, etc.) because a market basket analysis is one of the most powerful tools in uncovering customer or purchasing behavior!

⚡ Now tell me, would you explore this dataset?