Types of Data

TB: 5.1

Two Groups of Data

There are two group of data we can collect and analyze:

• Categorical

• Numerical

Categorical (Qualitative)

Categorical data involves non-numeric data points in a dataset. These data points fall into one of the following characteristics.

1. Ordinal (each data point can be sorted and ranked against each other)

   Example: Netflix Show's Personal Rating: hate, dislike, neutral, like, love

2. Nominal (each data point categorizes without inherent order, ranking, or level of strength)

   Example: Types of Dog Breeds: Westie, Samoyed, Golden Doodle

Numerical (Quantitative)

Numerical data involves numbers being collected for a dataset. The individual data points will have one of the following numeric characteristics.

1. Discrete

2. Continuous

Numerical Data Type will be studied in its own page.

The following is an example table containing qualitative data.

Netflix Shows Watched	Personal Rating
Brooklyn 99	Good
Superstore	Meh
Squid Game	Meh
Arcane	Good
Grey's Anatomy	Boring

Ordinal Categorical Data

With ordinal typed data, each data point can be ranked against each other.

In the example table, it is clear that the rankings go from Boring, Meh, to Good.

Key Points on Ordinal Data

• There must be ranking or distinct order

• The difference between each rank does not need to be numerically measurable

• The difference between each rank does not need to be evenly spaced out

• Numbers can be used as ordinal data, but it is only for ranking; the numbers should not provide mathematical analysis

Nominal Categorical Data

With nominal typed data, each data point contains information with no ranking and no measurable distance between each data point.

In the example table above, the column containing "Netflix Shows Watched" is our nominal data

Key Points on Nominal Data

• Nominal data is often collected for classification (example: favourite brand, participant's name, types of products produced in a factory)