Understanding Your Data: A Guide to Categories and Subcategories

Data can be categorized in various ways, depending on the context and purpose of analysis. Here are some common data categories and sub-categories:

1. Structured Data:

   • Tabular Data: Organized in rows and columns, often in databases or spreadsheets.
   • Time-Series Data: Recorded at regular intervals over time, like stock prices or weather data.
   • Hierarchical Data: Organized in a tree-like structure with parent-child relationships, such as organizational hierarchies.

2. Unstructured Data:

   • Text Data: Includes documents, emails, social media posts, and more.
   • Image Data: Comprises pictures, photographs, and other visual content.
   • Audio Data: Contains sound recordings, music, and spoken language.
   • Video Data: Consists of moving images and associated audio.

3. Semi-Structured Data:

   • XML (Extensible Markup Language): A markup language that allows data to be hierarchically structured.
   • JSON (JavaScript Object Notation): A lightweight data-interchange format often used for web APIs.
   • HTML (Hypertext Markup Language): Markup language for creating web pages, combining structure and content.

4. Quantitative Data:

   • Continuous Data: Represents values that can take any real number within a range, like height or temperature.
   • Discrete Data: Consists of distinct, separate values, such as the number of people in a household.

5. Qualitative Data:

   • Nominal Data: Categorizes data into non-numeric groups with no inherent order, like colors or types of fruits.
   • Ordinal Data: Orders categories but does not have consistent intervals between them, such as education levels or customer satisfaction ratings.

6. Geospatial Data:

   • GIS (Geographic Information System) Data: Contains information related to geographic locations, often in the form of maps.
   • GPS (Global Positioning System) Data: Provides precise location coordinates.

7. Biometric Data:

   • Fingerprint Data: Biometric information used for identification and authentication.
   • Retina/Iris Data: Used for eye-based recognition.
   • Facial Recognition Data: Analyzes facial features for identification.

8. Transactional Data:

   • Financial Data: Includes banking transactions, credit card purchases, and stock trading.
   • E-commerce Data: Records online purchase transactions.
   • Healthcare Data: Tracks patient medical records, prescriptions, and insurance claims.

9. Social Data:

   • Social Media Data: Contains user-generated content from platforms like Facebook, Twitter, and Instagram.
   • Opinion Data: Captures public sentiment and opinions through surveys or online reviews.

10. Sensor Data:

    • IoT (Internet of Things) Data: Collected by various sensors in smart devices, such as temperature sensors, motion detectors, and humidity sensors.

These are some of the common data categories and sub-categories. Depending on your specific analysis or project, you may encounter other categorizations or sub-divisions of data.

Data Category	Data Sub-Category	Examples
Structured Data	Tabular Data	Sales transactions, customer database
	Time-Series Data	Stock prices, weather records
	Hierarchical Data	Organizational chart, file directory
Unstructured Data	Text Data	Documents, tweets, emails
	Image Data	Photos, graphics, scanned documents
	Audio Data	Voice recordings, podcasts
	Video Data	Movies, video clips
Semi-Structured Data	XML	Config files, RSS feeds
	JSON	API responses, configuration data
	HTML	Web pages, email templates
Quantitative Data	Continuous Data	Temperature, height, weight
	Discrete Data	Number of customers, product counts
Qualitative Data	Nominal Data	Colors, car models, fruit types
	Ordinal Data	Education levels, survey ratings
Geospatial Data	GIS Data	Maps, spatial analysis data
	GPS Data	Location coordinates
Biometric Data	Fingerprint Data	Fingerprint scans for authentication
	Retina/Iris Data	Retinal scans for security
	Facial Recognition Data	Face scans for identity verification
Transactional Data	Financial Data	Bank transactions, credit card purchases
	E-commerce Data	Online purchase records
	Healthcare Data	Patient medical records, insurance claims
Social Data	Social Media Data	Facebook posts, Twitter tweets
	Opinion Data	Survey responses, online reviews
Sensor Data	IoT Data	Temperature sensor data, motion detector data
		Humidity sensor readings

Please note that these examples are meant to illustrate the different data categories and sub-categories and may not cover all possible examples within each category. Data types can vary widely in practice.