Data Analysis

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Data analysis is the act of transforming data with the aim of extracting useful information and facilitating conclusions. Depending on the type of data and the question, this might include application of Statistics methods, curve fitting, selecting or discarding certain subsets based on specific criterion, or other techniques.In contrast to Data mining, data analysis is usually more narrowly intended as not aiming to the discovery of unforeseen patterns hidden in the data, but to the verification or disproval of an existing model, or to the extraction of parameters necessary to adapt a theoretical model to (experimental) reality.

Applications in various fields Data analysis assumes different aspects, and possibly different names, in different fields.

Nuclear and particle physics In nuclear physics and particle physics the data usually originate from the particle detector via a Data acquisition system. It is then processed, in a step usually called data reduction, to apply calibrations and to extract physically significant information. Data reduction is most often, especially in large particle physics experiments, an automatic, batch-mode operation carried out by software written ad-hoc. The resulting data n-tuples are then scrutinized by the physicists, using specialized software tools like ROOT or Physics Analysis Workstation, comparing the results of the experiment with theory.

The theoretical models are often difficult to compare directly with the results of the experiments, so they are used instead as input for Monte Carlo method software like Geant4 that predict the response of the detector to a given theoretical event, producing simulated events which are then compared to experimental data.

See also: Computational physics.

Social sciences Qualitative data analysis (QDA) or qualitative research is the analysis of non-numerical data, for example words, photographs, observations, etc..

Information technology A special case is the Data analysis (information technology in othm ).

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