<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>1405-5546</journal-id>
<journal-title><![CDATA[Computación y Sistemas]]></journal-title>
<abbrev-journal-title><![CDATA[Comp. y Sist.]]></abbrev-journal-title>
<issn>1405-5546</issn>
<publisher>
<publisher-name><![CDATA[Instituto Politécnico Nacional, Centro de Investigación en Computación]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1405-55462020000300933</article-id>
<article-id pub-id-type="doi">10.13053/cys-24-3-3040</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Metodología multitareas automatizada para estudiar indicadores organizativos usando ciencia de datos]]></article-title>
<article-title xml:lang="en"><![CDATA[Automated-Multitasking Methodology to Study Business Indicators using Data Science]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Pérez Rave]]></surname>
<given-names><![CDATA[Jorge]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Jaramillo Álvarez]]></surname>
<given-names><![CDATA[Gloria]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[González Echavarría]]></surname>
<given-names><![CDATA[Favián]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Grupo de investigación IDINNOV  ]]></institution>
<addr-line><![CDATA[Medellín ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Nacional de Colombia Facultad de Minas ]]></institution>
<addr-line><![CDATA[Medellín ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad Nacional de Colombia Facultad de Minas Departamento de Ciencias de la Computación y de la Decisión]]></institution>
<addr-line><![CDATA[Medellín ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,Universidad de Antioquia Facultad de Ingeniería Departamento de Ingeniería Industrial]]></institution>
<addr-line><![CDATA[Medellín ]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>09</month>
<year>2020</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>09</month>
<year>2020</year>
</pub-date>
<volume>24</volume>
<numero>3</numero>
<fpage>933</fpage>
<lpage>956</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1405-55462020000300933&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1405-55462020000300933&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1405-55462020000300933&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen: El objetivo es proveer una metodología multitareas para estudiar indicadores organizativos de forma automática, usando Ciencia de Datos. Consta de 7 etapas: preparación de datos, análisis univariado, análisis bivariado, patrones de agrupación, reducción de dimensiones, entrenamiento de modelos y validación. Se usa R, RStudio (procesar) y Rmarkdown (visualizar). Se aplica en cuatro casos (dos de manufactura y dos de servicios) y aporta información de utilidad a nivel organizativo y de enseñanza-aprendizaje. La metodología distingue entre indicadores de medios y de respuesta, integra más de 10 métodos de análisis, abarca los tres alcances estadísticos (univariado, bivariado y multivariado) y responde seis preguntas de interés para los analistas.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract: The objective is to provide a multitasking methodology to study business indicators automatically using Data Science. This consists of 7 stages: data preparation, univariate analysis, bivariate analysis, grouping patterns, reduction of dimensions, supervised model training and validation. R, R-Studio (processing) and Rmarkdown (visualization) are used. The methodology is applied on four study cases (two from manufacturing sector and two from services) and provide useful information for firms and teaching-learning processes. The methodology distinguishes between media and response indicators, comprises more than 10 analysis methods, includes the three statistical scopes (univariate, bivariate and multivariate) and automatically answers six questions of interest for analysts.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Ciencia de datos]]></kwd>
<kwd lng="es"><![CDATA[indicadores de negocios]]></kwd>
<kwd lng="es"><![CDATA[programación en R]]></kwd>
<kwd lng="es"><![CDATA[analítica]]></kwd>
<kwd lng="es"><![CDATA[análisis de datos]]></kwd>
<kwd lng="es"><![CDATA[metodología de análisis]]></kwd>
<kwd lng="en"><![CDATA[Data science]]></kwd>
<kwd lng="en"><![CDATA[business indicators]]></kwd>
<kwd lng="en"><![CDATA[R-programming]]></kwd>
<kwd lng="en"><![CDATA[analytics]]></kwd>
<kwd lng="en"><![CDATA[data analysis]]></kwd>
<kwd lng="en"><![CDATA[analysis methodology]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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