Everything about Data Analysis totally explained
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
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.
Nuclear and particle physics
In
nuclear and
particle physics the data usually originate from the
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
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're used instead as input for
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 in information technology audits.
Business
See
Further Information
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