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Data

What is Data​

  • Collection of data objects and their attributes
  • An attribute is a property or characteristic of an object
    • Examples: eye color of a person, temperature, etc
    • Attribute is also known as variable, field, characteristic, or feature
  • A collection of attributes describe an object
    • Object is also known as record, point, case, sample, entity, or instance

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Types of Attributes​

  • Nominal - Examples: ID numbers, eye color, zip colors
  • Ordinal - Examples: rankings (e.g., taste of potato chips on a scale from 1-10), grades, height in {tall, medium, short}
  • Interval - Examples: calendar dates, temperatures in Celsius or Fahrenheit
  • Ratio - Examples: temperature in Kelvin, length, time, counts

Properties of Attribute Values​

The type of an attribute depends on which of the following properties it possesses:

  • Distinctness: equal to, not equal to
  • Order: < >
  • Addition/Subtraction: + -
  • Multiplication/Division: * /

Types​

  • Nominal attribute: distinctness
  • Ordinal attribute: distinctness & order
  • Interval attribute: distinctness, order & addition
  • Ratio attribute: all 4 properties

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Discrete and Continuous Attributes​

Discrete Attribute​

  • Has only a finite and countably infinite set of values
  • Examples: zip code, counts, or the set of words in a collection of documents
  • Often represented as integer variables
  • Note: binary attributes are a special case of discrete attributes

Continuous Attribute​

  • Has real numbers as attributes values
  • Examples: temperature, height, or weight
  • Practically, real values can only be measured and represented using a finite number of digits
  • Continuous attributes are typically represented as floating-point variables

The Limits of Data

Lessons Learned from Scaling to Multi-Terabyte Datasets – Build Great Things