Understanding data types: A coffee shop story

Making sense of your data without the statistical jargon

👋🏽 Hey, it’s Ismail. Welcome to data nomads lab newsletter on learning data analytics, career growth, networking, building portfolios, and interview skills to break into tech role as a high-performer.

I have been getting a lot of messages asking about data types, and I totally get it - this stuff can be confusing! So I thought, why not break it down using something we all love? Coffee! ☕

Let me walk you through different types of data using our coffee shop dataset. Trust me, it is going to be way more fun than those dry textbook explanations.

Quantitative vs Categorical: The big divide

Think about all the information you collect at a coffee shop. Some things you can count or measure (that is quantitative), and others you can put into categories (you guessed it - categorical).

Quantitative data is anything you can do math with:

• How many cups of coffee you sell each day (450 cups)

• The price of each drink ($3.77)

• How many minutes customers wait in line (3 minutes)

• The temperature of your coffee

Categorical data is anything that fits into groups:

• Types of drinks (latte, cappuccino, americano)

• Customer payment methods (cash, card, apple pay, bitcoin)

• Whether a customer ordered food with their drink (yes/no)

• Size of the drink (small, medium, large)

Diving deeper: Let us talk about subcategories

Here is where it gets interesting! Both quantitative and categorical data have their own subcategories. I know what you are thinking - Please, not more categories! But stick with me, I promise to keep it simple.

Quantitative data: Discrete vs Continuous

Discrete data is like counting whole things:

• Number of customers per day (you cannot have 45.7 customers)

• Number of espresso shots in a drink

• Number of pastries sold

Continuous data can take any value within a range:

• Exact drink temperature (94.6°F)

• Time spent in line (3.5 minutes)

• Amount spent ($4.75)

Categorical data: Nominal vs Ordinal

Nominal data is categories without any natural order:

• Drink types (a latte is not "more than" an americano)

• Payment methods (cash is not "greater than" card)

• Customer names (alphabetical order does not mean anything here)

Ordinal data has categories with a clear order:

• Drink sizes (small < medium < large)

• Customer satisfaction ratings (1 star < 2 stars < 3 stars)

• Loyalty levels (bronze < silver < gold)

Why does this matter?

You might be wondering why we need to care about all these types. Here is the deal - knowing your data types helps you:

1. Choose the right visualizations

   • You would not make a line graph for drink types, but it works great for daily sales

2. Pick the right analysis methods

   • You cannot calculate the average of payment methods

   • But you can definitely find the average waiting time

3. Make better business decisions

   • Understanding which type of data you are looking at helps you ask the right questions

   • It prevents you from making incorrect comparisons or calculations

Pop quiz: Test your understanding!

Before we look at our coffee shop data, let us test what you have learned! Below are 10 different types of data you might encounter in various business scenarios. Try to classify each one into the correct data type.

Remember our categories:

• Quantitative (Discrete or Continuous)

• Categorical (Nominal or Ordinal)

1. Zip Code

2. Age

3. Income

4. Marital Status (Single, Married, Divorced, etc.)

5. Height

6. Letter Grades (A+, A, A-, B+, B, B-, ...)

7. Travel Distance to Work

8. Ratings on a Survey (Poor, Ok, Great)

9. Temperature

10. Average Speed

Think about each one carefully! Consider:

• Can you do meaningful math with it?

• Are there natural ordered categories?

• Can it take any value within a range?

🤔 Take a moment to write down your answers and share it the comments below. I will share the answers once there are enough responses in the comments.

Real talk: Looking at our coffee shop data

Let us look at some actual fields from our coffee shop dataset and categorize them:

Order ID: Discrete quantitative (whole numbers only) 
Drink type: Nominal categorical 
Size: Ordinal categorical 
Price: Continuous quantitative 
Temperature: Continuous quantitative 
Customer rating: Ordinal categorical 
Time of order: Continuous quantitative

Pro tip

Here is a simple trick I use: If you can do meaningful math with it (add, subtract, find the average), it is quantitative. If you cannot, it is categorical. And if it is categorical, ask yourself: Does the order matter? If yes, it is ordinal. If not, it is nominal.

Want to dive deeper?

Ready to start working with these different data types? Head over to my earlier post 👉 Coffee shop statistics: real-world data made simple, where we dive into descriptive statistics using our coffee shop data. You will see exactly how understanding these data types helps us choose the right statistical methods and make sense of our business metrics.

Until then, try this: Next time you are at a coffee shop, look around and try to identify different types of data they might be collecting. It is a fun way to practice, and yes, I am that person who turns everything into a data exercise! 😄

Would love to hear your thoughts! What other real-world examples help you understand data types? Drop them in the comments below.

Stay curious,
Ismail Osman

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