Data analysis

Data analysis is the systematic inspection, cleaning, transformation, and modeling of data to answer questions, identify patterns, or support decisions.

Exploratory data analysis: The use of summaries and visualizations to investigate a dataset before formal modeling. It reveals structure, anomalies, and candidate explanations early in analysis.
Data: Recorded observations or measurements represented in a form that can be interpreted or processed. Analysis begins with observations whose meaning and structure must be understood.
Descriptive statistics: Methods that summarize and describe observed data. These methods condense datasets into interpretable summaries.
Data analytics: The broader practice of using data, statistical methods, and technology to inform decisions. Analytics often includes operational action beyond the analysis of data itself.
Data cleaning: The detection and correction of inaccurate, incomplete, duplicated, or inconsistent data. Cleaning reduces errors that can distort later summaries and models.
Variable (statistics): A characteristic that can take different values across observations. Variables define what is measured and compared in a dataset.
Predictive analytics: The use of data and models to estimate unknown or future outcomes. Prediction turns historical patterns into estimates for new cases.
Data mining: The computational discovery of useful patterns in large datasets. Mining emphasizes pattern discovery, while analysis also tests defined questions.
Data transformation: The conversion of data into formats or scales suited to analysis. Transformations make variables comparable and methods applicable.
Measurement: The assignment of values to properties according to defined rules. Measurement choices shape the values available for analysis.

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