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.
Ordinal (each data point can be sorted and ranked against each other)
Example: Netflix Show's Personal Rating: hate, dislike, neutral, like, love
Nominal (each data point categorizes with 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.
Discrete
Continuous
Numerical Data Type will be studied in its own page.
The following is an example table containing qualitative data.
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)
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