Raw data

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Origin_histograma_raw_data.png

Raw data

Raw data, also known as primary data, is data collected from a source that has not been subjected to processing or any other manipulation. This type of data is often used in scientific research, data analysis, and statistics to derive meaningful insights and conclusions.

Characteristics[edit | edit source]

Raw data is characterized by its unprocessed state. It is typically collected through various means such as surveys, experiments, observations, and sensor readings. Because it is unprocessed, raw data can contain errors, outliers, and noise that need to be addressed through data cleaning and data preprocessing techniques.

Collection Methods[edit | edit source]

Raw data can be collected through several methods:

  • Surveys: Questionnaires and interviews used to gather information from respondents.
  • Experiments: Controlled studies designed to test hypotheses.
  • Observations: Recording data based on direct observation of events or phenomena.
  • Sensors: Devices that collect data from the environment, such as temperature, humidity, and motion sensors.

Importance[edit | edit source]

Raw data is crucial for various fields, including scientific research, business analytics, and machine learning. It serves as the foundation for data analysis and helps in making informed decisions. By analyzing raw data, researchers and analysts can identify patterns, trends, and correlations that are essential for drawing conclusions and making predictions.

Processing[edit | edit source]

The processing of raw data involves several steps:

Applications[edit | edit source]

Raw data is used in various applications, including:

Challenges[edit | edit source]

Working with raw data presents several challenges:

See Also[edit | edit source]

References[edit | edit source]

External Links[edit | edit source]



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