Types of Data: Categorical vs Numerical Data

👉Sign up for Our Complete Data Science Training with 57% OFF: https://bit.ly/3sJATc9
👉 Download Our Free Data Science Career Guide: https://bit.ly/47Eh6d5

In this tutorial, we're going to explore 2 types of data: Categorical Data and Numerical Data.

So, categorical data describes categories or groups. One example is car brands like Mercedes, BMW and Audi – they show different categories. Another instance is answers to yes and no questions. If we ask questions like:
-Are you currently enrolled in a university?
Or
-Do you own a car?
Yes and no would be the two groups of answers that can be obtained.
This is categorical data.

On the other hand, numerical data, as its name suggests, represents numbers. It is further divided into two subsets: discrete and continuous.
Discrete data can usually be counted in a finite matter. A good example would be the number of children that you want to have. Even if you don’t know exactly how many, you are absolutely sure that the value will be an integer such as 0, 1, 2, or even 10.

Continuous data is infinite and impossible to count. For instance, your weight can take on every value in some range!

► Consider hitting the SUBSCRIBE button if you LIKE the content: https://www.youtube.com/c/365DataScie...

► VISIT our website: https://bit.ly/365ds

🤝 Connect with us LinkedIn:   / 365datascience  

365 Data Science is an online educational career website that offers the incredible opportunity to find your way into the data science world no matter your previous knowledge and experience. We have prepared numerous courses that suit the needs of aspiring BI analysts, Data analysts and Data scientists.

We at 365 Data Science are committed educators who believe that curiosity should not be hindered by inability to access good learning resources. This is why we focus all our efforts on creating high-quality educational content which anyone can access online.

Check out our Data Science Career guides:    • How to Become a... (Data and AI Caree...  

#TYPESOFDATA #CATEGORICALDATA #NUMERICALDATA