![]() However, the repercussions of neglecting it are so serious that if someone uses your personal data without your permission, you can face a class-action lawsuit. ![]() In the meantime, you can always check the following diagram whenever you need a handy reference:ĭata sensitivity is a controversial matter with many loose ends yet to be tied. It gets better over time and the key to progress is practice. Now, it’s alright if you feel confused and can’t clearly differentiate between these different types of data just yet. But can we apply the same concept to a discrete value, such as kids in a school? That would be more than unreasonable, as you can’t possibly divide a kid in half or into smaller units, right? ![]() However, this is not possible with discrete data, as dividing them into smaller units will give us unreasonable values.įor example, weight is continuous because we can measure it in kilograms, grams, and milligrams and still we have a valid weight value. The difference is that we can split continuous data further into smaller units, and they still make sense. On the other hand, quantitative data deals with numeric values on which we can apply mathematical operations – height, fruits in a basket, kids in a school.Īlthough they seem similar, here’s something else to keep in mind – quantitative data can be continuous or discrete. Consider gender, city, employment status, colors, etc. Nominal data, however, doesn’t follow a specific order like ordinal data. Ordinal data follows a specific order or ranking, as in test grades, economic status, or military rank. Qualitative data is then classified into 2 other subtypes – “ordinal” and “nominal”. Examples include colors, plants, and places. It’s also known as categorical data because, as the name implies, you can label a group of items or data points to a specific category. Qualitative data usually describes an object or a group of items. Generally speaking, we can classify data into 2 main types: qualitative (categorical) data and quantitative (numerical) data. Data Types According to the Sensitivity Level.But for now, let’s focus on exploring the different types of data with this beginner-friendly guide. And if you already understand the concept of data and you're looking to develop a career in data science, then Intro to Data and Data Science is the right online course for you. ![]() That said, if you’re still new to data and you want to understand how to read and interpret it, you can start with the Data Literacy course. So, how can you make the most of your data? The answer is simple: the first step when it comes to dealing with data is knowing its type and properties. Just like petrol, without the right technique and tools to extract it, you’ll never be able to use it to the fullest. However, if you don’t know how to manage, protect and clean this huge amount of data, it becomes completely worthless. According to Mongo DB’s APAC Senior Vice President, Simon Eid, there are over 40 zettabytes of data in the world today, which is equivalent to 40 trillion gigabytes of data! And although it’s abundant, it’s still so valuable that it’s considered the fuel for all industries, from healthcare to transportation.
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