Data analysis
{{Computational physics}}
'''Data analysis''' is the process of looking at and summarizing '''[[data]]''' with the intent to extract useful [[information]] and develop conclusions.  Data analysis is closely related to [[data mining]], but data mining tends to focus on larger data sets, with less emphasis on making [[inference]], and often uses data that was originally collected for a different purpose.  In [[statistics|statistical applications]], some people divide data analysis into [[descriptive statistics]], [[exploratory data analysis]] and [[confirmatory data analysis]], where the EDA focuses on discovering new features in the data, and CDA on confirming or falsifying existing hypotheses.

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

The term ''data analysis'' is also used as a synonym for [[data modeling]], which is unrelated to the subject of this article.

==Nuclear and particle physics==
In [[nuclear physics|nuclear]] and [[particle physics]] the data usually originate from the [[particle detector|experimental apparatus]] 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|PAW]], 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|Monte Carlo simulation]] 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 )|data analysis in information technology audits]].

==Business==
See 
* [[Analytics]]
* [[Business intelligence]]
* [[Data mining]]

==See also==
<div style="-moz-column-count:2; column-count:2;">
*[[Censoring (statistics)]]
*[[Data acquisition]]
*[[Data governance]]
*[[Data mining]]
*[[Exploratory data analysis]]
*[[Predictive analytics]]
*[[Qualitative research]]
*[[Scientific computing]]
*[[Test method]]
*[[Structured data analysis (statistics)]]
</div>

==Further reading==
*Michael S. Lewis-Beck, ''Data Analysis: an Introduction'', Sage Publications Inc, 1995, ISBN 0803957726
*Pyzdek, T, "Quality Engineering Handbook", 2003, ISBN 0824746147 
*Godfrey, A. B., "Juran's Quality Handbook", 1999, ISBN 007034003 
*"Engineering Statistics Handbook", NIST/SEMATEK, [http://www.itl.nist.gov/div898/handbook/]
*"Manual on Presentation of Data and Control Chart Analysis", ASTM MNL 7, 1990, ISBN:0-8031-1189-0

[[Category:Data analysis| ]]
[[Category:Scientific method]]
[[Category:Particle physics]]

[[eo:Datuma analitiko]]
[[es:Análisis de datos]]
[[fr:Analyse des données (statistiques)]]
[[pl:Analiza danych]]
[[fi:Data-analyysi]]