Types of Data

Understanding the types of data is key to processing and analyzing it effectively. Broadly, data falls into two main categories: Quantitative and Qualitative.

Quantitative Data

Quantitative data deals with numbers and measurable forms. It can be further classified as Discrete or Continuous.

• Measurable values (e.g., memory usage, CPU usage, number of likes, shares, retweets)
• Collected from the real world
• Usually close-ended

Discrete

• Represented by whole numbers
• Countable and finite

Example:

• Number of cameras in a phone
• Memory size in GB

Continuous

• Can take fractional values
• Often involve measurements

Example:

• CPU usage (%)
• Memory usage over time
• Temperature readings

Qualitative Data

Qualitative data describes qualities or characteristics that can’t be easily measured numerically.

• Descriptive or abstract
• Can come from text, audio, or images
• Collected via interviews, surveys, or observations
• Usually open-ended

Examples

• Gender: Male, Female, Non-Binary, etc.
• Smartphones: iPhone, Pixel, Motorola, etc.

Nominal

Categorical data without any intrinsic order

Examples:

• Red, Blue, Green
• Types of fruits: Apple, Banana, Mango

Can you rank them logically? No — that’s what makes them nominal.

Ordinal

Categorical data with a meaningful order

Examples:

• T-shirt sizes: Small, Medium, Large
• Grading system: A, B, C, D, F

There’s a hierarchy, but the intervals aren’t equal.

Abstract Understanding

Some qualitative data comes from non-traditional sources like:

• Conversations
• Audio or video files
• Observations or open-text survey responses

This type of data often requires interpretation before it’s usable in models or analysis.