AI - Types of Data
1. Structured Data • Everything is organized into fixed fields (like a spreadsheet). • Easy for computers to read and search. • Stored usually in relational databases (SQL). Examples: • Bank transactions (Date, Amount, Payee) • Student grades (Name, Subject, Score) ⸻ 2. Unstructured Data • No fixed format or organization. • Hard for machines to search directly — needs special tools (like NLP or Computer Vision). • Makes up ~80% of all data in the world! Examples: • A video recording (no obvious fields) • A random tweet or Instagram post • An image from a security camera ⸻ 3. Semi-Structured Data • Partly organized — has some tags, keys, or structure, but not as rigid as a table. • Flexible format — easier to manage than unstructured data, but not fully structured. Examples: • JSON from an API, XML documents Structured, Unstructured, and Semi-structured Data Discrete v/s Continuous Data Measurement Scales for Data
1. Structured Data
• Everything is organized into fixed fields (like a spreadsheet).
• Easy for computers to read and search.
• Stored usually in relational databases (SQL).
Examples:
• Bank transactions (Date, Amount, Payee)
• Student grades (Name, Subject, Score)
⸻
2. Unstructured Data
• No fixed format or organization.
• Hard for machines to search directly — needs special tools (like NLP or Computer Vision).
• Makes up ~80% of all data in the world!
Examples:
• A video recording (no obvious fields)
• A random tweet or Instagram post
• An image from a security camera
⸻
3. Semi-Structured Data
• Partly organized — has some tags, keys, or structure, but not as rigid as a table.
• Flexible format — easier to manage than unstructured data, but not fully structured.
Examples:
• JSON from an API, XML documents
Structured, Unstructured, and Semi-structured Data
Discrete v/s Continuous Data
Measurement Scales for Data
