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{{Computational physics}}
'''Data analysis''' is the process of inspecting, [[Data cleansing|cleansing]], [[Data transformation|transforming]], and [[Data modeling|modeling]] [[data]] with the goal of discovering useful information, informing conclusions, and supporting [[decision-making]].<ref name="Auerbach Publications">{{Citation|title=Transforming Unstructured Data into Useful Information|date=2014-03-12|url=http://dx.doi.org/10.1201/b16666-14|work=Big Data, Mining, and Analytics|pages=227–246|publisher=Auerbach Publications|doi=10.1201/b16666-14|isbn=978-0-429-09529-0|access-date=2021-05-29}}</ref> Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains.<ref>{{Citation|title=The Multiple Facets of Correlation Functions |url=http://dx.doi.org/10.1017/9781108241922.013 |work=Data Analysis Techniques for Physical Scientists|year=2017|pages=526–576|publisher=Cambridge University Press|doi=10.1017/9781108241922.013|isbn=978-1-108-41678-8|access-date=2021-05-29}}</ref> In today's business world, data analysis plays a role in making decisions more scientific and helping businesses operate more effectively.<ref>Xia, B. S., & Gong, P. (2015). Review of business intelligence through data analysis. ''Benchmarking'', ''21''(2), 300-311. {{doi|10.1108/BIJ-08-2012-0050}}</ref>
[[Data mining]] is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while [[business intelligence]] covers data analysis that relies heavily on aggregation, focusing mainly on business information. In statistical applications, data analysis can be divided into [[descriptive statistics]], [[exploratory data analysis]] (EDA), and [[Statistical hypothesis testing|confirmatory data analysis]] (CDA).<ref>{{Citation|title=Data Coding and Exploratory Analysis (EDA) Rules for Data Coding Exploratory Data Analysis (EDA) Statistical Assumptions|date=2004-08-16 |url=http://dx.doi.org/10.4324/9781410611420-6|work=SPSS for Intermediate Statistics|pages=42–67 |publisher=Routledge|doi=10.4324/9781410611420-6|isbn=978-1-4106-1142-0|access-date=2021-05-29}}</ref> EDA focuses on discovering new features in the data while CDA focuses on confirming or falsifying existing [[hypotheses]].<ref>{{Cite book |last1=Samandar|first1=Petersson|first2=Sofia|last2=Svantesson|title=Skapandet av förtroende inom eWOM : En studie av profilbildens effekt ur ett könsperspektiv |date=2017|publisher=Högskolan i Gävle, Företagsekonomi|oclc=1233454128}}</ref> [[Predictive analytics]] focuses on the application of statistical models for predictive forecasting or classification, while [[text analytics]] applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a variety of [[unstructured data]]. All of the above are varieties of data analysis.<ref>{{Cite journal|last=Goodnight|first=James|date=2011-01-13 |title=The forecast for predictive analytics: hot and getting hotter |url=http://dx.doi.org/10.1002/sam.10106|journal=Statistical Analysis and Data Mining: The ASA Data Science Journal|volume=4|issue=1|pages=9–10|doi=10.1002/sam.10106|s2cid=38571193 |issn=1932-1864}}</ref>
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