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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
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.
graph TD A[Types of Data] A --> B[Quantitative] A --> C[Qualitative] B --> B1[Discrete] B --> B2[Continuous] C --> C1[Nominal] C --> C2[Ordinal]
| Category | Subtype | Description | Examples |
|---|---|---|---|
| Quantitative | Discrete | Whole numbers, countable | Number of phones, number of users |
| Continuous | Measurable, can take fractional values | Temperature, CPU usage | |
| Qualitative | Nominal | Categorical with no natural order | Gender, Colors (Red, Blue, Green) |
| Ordinal | Categorical with a meaningful order | T-shirt sizes (S, M, L), Grades (A, B, C…) |
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.
#quantitative #qualitative #discrete #continuous #nominal #ordinal