{"id":63904,"date":"2021-05-21T08:41:36","date_gmt":"2021-05-21T07:41:36","guid":{"rendered":"https:\/\/datascientest.com\/?p=63904"},"modified":"2024-07-31T15:13:42","modified_gmt":"2024-07-31T15:13:42","slug":"data-cleaning-definition","status":"publish","type":"post","link":"https:\/\/datascientest.com\/data-cleaning-definition","title":{"rendered":"Data Cleaning : d\u00e9finition, techniques, importance en Data Science"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"63904\" class=\"elementor elementor-63904\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-d956474 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"d956474\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-f08a549\" data-id=\"f08a549\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-ab86b41 elementor-widget elementor-widget-heading\" data-id=\"ab86b41\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-heading-title{padding:0;margin:0;line-height:1}.elementor-widget-heading .elementor-heading-title[class*=elementor-size-]>a{color:inherit;font-size:inherit;line-height:inherit}.elementor-widget-heading .elementor-heading-title.elementor-size-small{font-size:15px}.elementor-widget-heading .elementor-heading-title.elementor-size-medium{font-size:19px}.elementor-widget-heading .elementor-heading-title.elementor-size-large{font-size:29px}.elementor-widget-heading .elementor-heading-title.elementor-size-xl{font-size:39px}.elementor-widget-heading .elementor-heading-title.elementor-size-xxl{font-size:59px}<\/style><h2 class=\"elementor-heading-title elementor-size-default\">Le Data Cleaning ou nettoyage de donn\u00e9es est une \u00e9tape indispensable en Data Science et en Machine Learning. Elle consiste \u00e0 r\u00e9soudre les probl\u00e8mes dans les ensembles de donn\u00e9es, afin de pouvoir les exploiter par la suite. D\u00e9finitions, techniques, cas d'usage, formations...<\/h2>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-b5a07f2 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"b5a07f2\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-41bae73\" data-id=\"41bae73\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-f98b80d elementor-widget elementor-widget-text-editor\" data-id=\"f98b80d\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-text-editor.elementor-drop-cap-view-stacked .elementor-drop-cap{background-color:#69727d;color:#fff}.elementor-widget-text-editor.elementor-drop-cap-view-framed .elementor-drop-cap{color:#69727d;border:3px solid;background-color:transparent}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap{margin-top:8px}.elementor-widget-text-editor:not(.elementor-drop-cap-view-default) .elementor-drop-cap-letter{width:1em;height:1em}.elementor-widget-text-editor .elementor-drop-cap{float:left;text-align:center;line-height:1;font-size:50px}.elementor-widget-text-editor .elementor-drop-cap-letter{display:inline-block}<\/style>\t\t\t\t<p>Les <strong>donn\u00e9es sont indispensables en Data Science,<\/strong> en <strong>intelligence artificielle<\/strong> et en <strong>Machine Learning<\/strong>. Elles sont en quelque sorte le carburant de ces technologies.<\/p><p>Par cons\u00e9quent, il est tr\u00e8s important <strong>d&rsquo;assurer la qualit\u00e9 des donn\u00e9es<\/strong>. Il est aujourd&rsquo;hui tr\u00e8s facile de trouver des donn\u00e9es de bonne qualit\u00e9, propres et structur\u00e9es, sur des Marketplaces d\u00e9di\u00e9es. En revanche, pour une entreprise nettoyer ses propres donn\u00e9es internes, une entreprise doit recourir au Data Cleaning.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-71f3904 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"71f3904\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-423d7dd\" data-id=\"423d7dd\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-31fe591 elementor-widget elementor-widget-video\" data-id=\"31fe591\" data-element_type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/www.youtube.com\\\/watch?v=q1Yos0JxY3w&quot;,&quot;video_type&quot;:&quot;youtube&quot;,&quot;controls&quot;:&quot;yes&quot;}\" data-widget_type=\"video.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<style>\/*! elementor - v3.15.0 - 02-08-2023 *\/\n.elementor-widget-video .elementor-widget-container{overflow:hidden;transform:translateZ(0)}.elementor-widget-video .elementor-wrapper{aspect-ratio:var(--video-aspect-ratio)}.elementor-widget-video .elementor-wrapper iframe,.elementor-widget-video .elementor-wrapper video{height:100%;width:100%;display:flex;border:none;background-color:#000}@supports not (aspect-ratio:1\/1){.elementor-widget-video .elementor-wrapper{position:relative;overflow:hidden;height:0;padding-bottom:calc(100% \/ var(--video-aspect-ratio))}.elementor-widget-video .elementor-wrapper iframe,.elementor-widget-video .elementor-wrapper video{position:absolute;top:0;right:0;bottom:0;left:0}}.elementor-widget-video .elementor-open-inline .elementor-custom-embed-image-overlay{position:absolute;top:0;right:0;bottom:0;left:0;background-size:cover;background-position:50%}.elementor-widget-video .elementor-custom-embed-image-overlay{cursor:pointer;text-align:center}.elementor-widget-video .elementor-custom-embed-image-overlay:hover .elementor-custom-embed-play i{opacity:1}.elementor-widget-video .elementor-custom-embed-image-overlay img{display:block;width:100%;aspect-ratio:var(--video-aspect-ratio);-o-object-fit:cover;object-fit:cover;-o-object-position:center center;object-position:center center}@supports not (aspect-ratio:1\/1){.elementor-widget-video .elementor-custom-embed-image-overlay{position:relative;overflow:hidden;height:0;padding-bottom:calc(100% \/ var(--video-aspect-ratio))}.elementor-widget-video .elementor-custom-embed-image-overlay img{position:absolute;top:0;right:0;bottom:0;left:0}}.elementor-widget-video .e-hosted-video .elementor-video{-o-object-fit:cover;object-fit:cover}.e-con-inner>.elementor-widget-video,.e-con>.elementor-widget-video{width:var(--container-widget-width);--flex-grow:var(--container-widget-flex-grow)}<\/style>\t\t<div class=\"elementor-wrapper elementor-open-inline\">\n\t\t\t<div class=\"elementor-video\"><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-abb8ecb elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"abb8ecb\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-f05b192\" data-id=\"f05b192\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-c4dd84d elementor-widget elementor-widget-heading\" data-id=\"c4dd84d\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Qu'est-ce que le Data Cleaning ?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-9536c0f elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"9536c0f\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-e3e89cc\" data-id=\"e3e89cc\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-c806bcd elementor-widget elementor-widget-text-editor\" data-id=\"c806bcd\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Le Data Cleaning (<strong>aussi appel\u00e9 Data Cleansing ou Data Scrubbing<\/strong>) englobe plusieurs processus ayant pour but d&rsquo;am\u00e9liorer la qualit\u00e9 des donn\u00e9es. Il existe de nombreux outils et des pratiques permettant d&rsquo;\u00e9liminer les probl\u00e8mes dans un dataset.<\/p><p>Ces processus servent \u00e0 corriger ou \u00e0 <strong>supprimer les enregistrements inexacts<\/strong> dans une base de donn\u00e9es ou un ensemble de donn\u00e9es. De mani\u00e8re g\u00e9n\u00e9rale, il s&rsquo;agit d&rsquo;identifier et de remplacer des donn\u00e9es ou des enregistrements incomplets, inexacts, corrompus ou manquant de pertinence.<\/p><p>\u00c0 l&rsquo;issue d&rsquo;un Data Cleaning correctement effectu\u00e9, tous les ensembles de donn\u00e9es devraient \u00eatre <strong>coh\u00e9rents et d\u00e9pourvus d&rsquo;erreurs<\/strong>. C&rsquo;est indispensable pour l&rsquo;utilisation et l&rsquo;exploitation de ces donn\u00e9es.<\/p><p>Sans nettoyage,<strong> les r\u00e9sultats des analyses risquent d&rsquo;\u00eatre fauss\u00e9s<\/strong>. De m\u00eame, un mod\u00e8le de Machine Learning ou d&rsquo;IA entra\u00een\u00e9 \u00e0 partir de mauvaises donn\u00e9es peut \u00eatre biais\u00e9 ou d\u00e9livrer de pi\u00e8tres performances.<\/p><p>Le <strong>Data Cleaning est diff\u00e9rent de la Data Transformation<\/strong>. Le Cleaning consiste \u00e0 convertir les donn\u00e9es dans un format vers un autre, tandis que la Transformation (aussi appel\u00e9e Wrangling ou Munging) vise \u00e0 convertir les donn\u00e9es brutes dans un format adapt\u00e9 \u00e0 l&rsquo;analyse.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-7f294ac elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"7f294ac\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-e92899d\" data-id=\"e92899d\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-45d6fb4 elementor-widget elementor-widget-video\" data-id=\"45d6fb4\" data-element_type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/www.youtube.com\\\/watch?v=MIehKREnLdM&quot;,&quot;video_type&quot;:&quot;youtube&quot;,&quot;controls&quot;:&quot;yes&quot;}\" data-widget_type=\"video.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-wrapper elementor-open-inline\">\n\t\t\t<div class=\"elementor-video\"><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-562fc67 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"562fc67\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-acaa390\" data-id=\"acaa390\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-1dd2688 elementor-widget elementor-widget-heading\" data-id=\"1dd2688\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">A quoi sert le Data Cleaning ?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-fee4fe8 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"fee4fe8\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-278211c\" data-id=\"278211c\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-748ce36 elementor-widget elementor-widget-text-editor\" data-id=\"748ce36\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Les donn\u00e9es sont d\u00e9sormais<strong> une ressource essentielle pour les entreprises<\/strong> de tous les secteurs. \u00c0 l&rsquo;heure du Big Data, elles sont utilis\u00e9es comme support pour la prise de d\u00e9cisions cruciales.<\/p><p>Selon une \u00e9tude men\u00e9e par IBM, <strong>la mauvaise qualit\u00e9 des donn\u00e9es co\u00fbte d\u00e9sormais 3,1 billions de dollars<\/strong> par an aux Etats-Unis. Et ce co\u00fbt augmente de mani\u00e8re exponentielle.<\/p><p>La<strong> pr\u00e9vention par le biais du Data Cleaning<\/strong> est relativement abordable, mais la correction de probl\u00e8mes d\u00e9j\u00e0 existants peut co\u00fbter dix fois plus cher. Pire encore, la correction d&rsquo;un probl\u00e8me dans les donn\u00e9es apr\u00e8s qu&rsquo;il ait caus\u00e9 une panne se r\u00e9v\u00e8le cent fois plus on\u00e9reuse.<\/p><p>Une<strong> large vari\u00e9t\u00e9 de probl\u00e8mes<\/strong> peut survenir \u00e0 cause de donn\u00e9es de basse qualit\u00e9. Une campagne marketing risque par exemple d&rsquo;\u00eatre mal cibl\u00e9e et donc d&rsquo;\u00e9chouer.<\/p><p>Dans <strong>le domaine de la sant\u00e9<\/strong>, les mauvaises donn\u00e9es peuvent conduire \u00e0 des traitements mal appropri\u00e9s et m\u00eame \u00e0 l&rsquo;\u00e9chec de la cr\u00e9ation de m\u00e9dicaments. Une \u00e9tude men\u00e9e par Accenture r\u00e9v\u00e8le que le manque de donn\u00e9es propres est le principal obstacle \u00e0 l&rsquo;adoption de l&rsquo;IA dans ce domaine.<\/p><p>En logistique, les donn\u00e9es peuvent poser <strong>des probl\u00e8mes d&rsquo;inventaire<\/strong>, de planification des livraisons et donc nuire \u00e0 la satisfaction des clients. C\u00f4t\u00e9 manufacture, les usines configurant des robots avec de mauvaises donn\u00e9es s&rsquo;exposent \u00e0 de graves probl\u00e8mes.<\/p><p>Notons enfin que le Data Cleaning est requis pour se conformer aux r\u00e8gles de confidentialit\u00e9 impos\u00e9es par <strong>les lois comme le RGPD<\/strong>. Quel que soit le secteur, cette pratique permet donc d&rsquo;\u00e9viter d&rsquo;importants probl\u00e8mes.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-9c48721 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"9c48721\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-fcc9c27\" data-id=\"fcc9c27\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-4bb08dd elementor-widget elementor-widget-video\" data-id=\"4bb08dd\" data-element_type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/www.youtube.com\\\/watch?v=iMD3pWAFXEY&amp;t=14s&quot;,&quot;video_type&quot;:&quot;youtube&quot;,&quot;controls&quot;:&quot;yes&quot;}\" data-widget_type=\"video.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-wrapper elementor-open-inline\">\n\t\t\t<div class=\"elementor-video\"><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-b21619e elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"b21619e\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-8e03a53\" data-id=\"8e03a53\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-cf3d47a elementor-widget elementor-widget-heading\" data-id=\"cf3d47a\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Les avantages du Data Cleaning<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-c24f15f elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"c24f15f\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-baea7de\" data-id=\"baea7de\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-012453f elementor-widget elementor-widget-text-editor\" data-id=\"012453f\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Le Data Cleaning offre de nombreux avantages. L&rsquo;un des principaux bienfaits est de permettre <strong>une meilleure prise de d\u00e9cision bas\u00e9e sur les donn\u00e9es<\/strong>.<\/p><p>Une<strong> qualit\u00e9 sup\u00e9rieure impacte positivement<\/strong> toutes les activit\u00e9s impliquant les donn\u00e9es. Or, la Data prend une importance croissante dans tous les secteurs.<\/p><p>Pour tirer pleinement profit de cette pratique, le Data Cleaning doit \u00eatre per\u00e7u comme <strong>un effort \u00e0 l&rsquo;\u00e9chelle de toute l&rsquo;entreprise<\/strong>. Il permet de fluidifier les op\u00e9rations de l&rsquo;entreprise, mais aussi d&rsquo;accro\u00eetre la productivit\u00e9 puisque les \u00e9quipes n&rsquo;ont plus \u00e0 perdre du temps sur des donn\u00e9es incorrectes.<\/p><p>Les <strong>ventes peuvent augmenter<\/strong> si les \u00e9quipes marketing ont acc\u00e8s aux meilleures donn\u00e9es. Le cumul de ces diff\u00e9rents bienfaits en interne et en externe m\u00e8ne \u00e0 une rentabilit\u00e9 accrue.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-6045f6e elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"6045f6e\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-7872acc\" data-id=\"7872acc\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-6d86b2c elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"6d86b2c\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/formation-data-engineer\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">Suivre une formation Data Cleaning<\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-36de009 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"36de009\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-9276025\" data-id=\"9276025\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-b23ca02 elementor-widget elementor-widget-heading\" data-id=\"b23ca02\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Les diff\u00e9rents types de probl\u00e8mes de donn\u00e9es<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-2ac89df elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"2ac89df\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-895a463\" data-id=\"895a463\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-61e339a elementor-widget elementor-widget-text-editor\" data-id=\"61e339a\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Les entreprises collectent une large vari\u00e9t\u00e9 de donn\u00e9es, en provenance<strong> de multiples sources<\/strong>. Ces informations peuvent \u00eatre collect\u00e9es directement en interne ou aupr\u00e8s des clients, ou m\u00eame captur\u00e9es depuis le web et les r\u00e9seaux sociaux.<\/p><p>Or, au cours de ce proc\u00e9d\u00e9, <strong>diff\u00e9rents probl\u00e8mes peuvent survenir<\/strong>. Un dataset peut tout d&rsquo;abord contenir des donn\u00e9es dupliqu\u00e9es, \u00e0 savoir plusieurs enregistrements identiques.<\/p><p>Les<strong> donn\u00e9es peuvent aussi \u00eatre en conflit<\/strong>. Un ensemble peut contenir plusieurs enregistrements similaires avec des attributs diff\u00e9rents.<\/p><p>Au contraire, il arrive qu&rsquo;il<strong> manque des attributs de donn\u00e9es<\/strong>. Les donn\u00e9es peuvent aussi ne pas \u00eatre conformes \u00e0 la r\u00e9glementation.<\/p><p>Ces probl\u00e8mes peuvent \u00eatre caus\u00e9s par diff\u00e9rents sources. Il peut s&rsquo;agir d&rsquo;un <strong>souci de synchronisation<\/strong>, lorsque les donn\u00e9es ne sont pas correctement partag\u00e9es entre deux syst\u00e8mes.<\/p><p>Une autre cause peut \u00eatre <strong>un bug logiciel dans les applications<\/strong> de traitement de donn\u00e9es. Les informations peuvent \u00eatre \u00a0\u00bb \u00e9crites \u00a0\u00bb avec des erreurs, tandis que les donn\u00e9es correctes peuvent \u00eatre remplac\u00e9es par accident.<\/p><p>Enfin,<strong> la cause peut tout simplement \u00eatre humaine<\/strong>. Les consommateurs peuvent fournir d\u00e9lib\u00e9r\u00e9ment des donn\u00e9es incompl\u00e8tes ou incorrectes, afin de prot\u00e9ger leur confidentialit\u00e9.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-4ad77c0 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"4ad77c0\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-3334ea5\" data-id=\"3334ea5\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-e51144c elementor-widget elementor-widget-video\" data-id=\"e51144c\" data-element_type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/www.youtube.com\\\/watch?v=A4v6hKD-qcU&quot;,&quot;video_type&quot;:&quot;youtube&quot;,&quot;controls&quot;:&quot;yes&quot;}\" data-widget_type=\"video.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-wrapper elementor-open-inline\">\n\t\t\t<div class=\"elementor-video\"><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-5f2c53f elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"5f2c53f\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-e735127\" data-id=\"e735127\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-8674a5d elementor-widget elementor-widget-heading\" data-id=\"8674a5d\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Quelles sont les caract\u00e9ristiques de donn\u00e9es de haute qualit\u00e9 ?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-8cf9bc7 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"8cf9bc7\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-cdc252a\" data-id=\"cdc252a\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-6701621 elementor-widget elementor-widget-text-editor\" data-id=\"6701621\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Pour \u00eatre consid\u00e9r\u00e9es de haute qualit\u00e9, les donn\u00e9es doivent r\u00e9pondre \u00e0 plusieurs crit\u00e8res. Elles <strong>doivent \u00eatre \u00ab\u00a0valides\u00a0\u00bb<\/strong>, ce qui signifie qu&rsquo;elles correspondent aux r\u00e8gles et contraintes fix\u00e9es par l&rsquo;entreprise. Il peut s&rsquo;agir de contraintes de types de donn\u00e9es, de valeurs, ou d&rsquo;organisation des donn\u00e9es dans les databases.<\/p><p>Les <strong>donn\u00e9es de qualit\u00e9<\/strong> doivent aussi \u00eatre exactes, compl\u00e8tes, coh\u00e9rentes, uniformes et tra\u00e7ables. Telles sont les caract\u00e9ristiques impactant la Data Quality, et pouvant \u00eatre corrig\u00e9es gr\u00e2ce au Data Cleaning.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-83da04d elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"83da04d\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-7fd4c7c\" data-id=\"7fd4c7c\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-8b2115e elementor-widget elementor-widget-heading\" data-id=\"8b2115e\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Les \u00e9tapes du Data Cleaning<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-f3dacd9 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"f3dacd9\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-7c48680\" data-id=\"7c48680\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-b6c67a4 elementor-widget elementor-widget-text-editor\" data-id=\"b6c67a4\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Pour \u00eatre efficace, le Data Cleaning doit \u00eatre envisag\u00e9 comme<strong> un processus par \u00e9tapes<\/strong>. Pour commencer, il convient d&rsquo;\u00e9tablir un plan de qualit\u00e9 des donn\u00e9es.<\/p><p>Ce plan consiste \u00e0 <strong>identifier la source principale<\/strong> des erreurs et probl\u00e8mes, et \u00e0 d\u00e9terminer comment y rem\u00e9dier. Les mesures correctives doivent \u00eatre r\u00e9parties entre les responsables ad\u00e9quats.<\/p><p>En outre,<strong> des m\u00e9triques doivent \u00eatre choisies<\/strong> pour mesurer la qualit\u00e9 des donn\u00e9es de fa\u00e7on claire et concise. Ceci permettra par la suite de prioriser les initiatives de Data Cleaning.<\/p><p>Enfin, <strong>un ensemble d&rsquo;actions et de mesures<\/strong> \u00e0 prendre doivent \u00eatre identifi\u00e9s pour lancer le processus. Ces mesures seront mises \u00e0 jour au fil du temps, des changements dans la qualit\u00e9 des donn\u00e9es et de l&rsquo;\u00e9volution de l&rsquo;entreprise.<\/p><p>La deuxi\u00e8me \u00e9tape consiste \u00e0 <strong>corriger les donn\u00e9es \u00e0 la source<\/strong>, avant m\u00eame qu&rsquo;elles soient incorpor\u00e9es au syst\u00e8me sous une forme erron\u00e9e. Cette pratique permet d&rsquo;\u00e9conomiser du temps et de l&rsquo;\u00e9nergie et de rem\u00e9dier aux probl\u00e8mes avant qu&rsquo;il ne soit trop tard.<\/p><p>Par la suite, il est important de <strong>mesurer l&rsquo;exactitude des donn\u00e9es<\/strong> en temps r\u00e9el. Il existe diff\u00e9rents outils et techniques pr\u00e9vus \u00e0 cet effet.<\/p><p>Si vous ne parvenez malheureusement pas \u00e0 supprimer les duplicatas \u00e0 la source, il est important de <strong>les d\u00e9tecter et de les supprimer activement<\/strong> par la suite. Vous devez aussi standardiser, normaliser, fusionner, agr\u00e9ger et filtrer les donn\u00e9es.<\/p><p>Enfin, la derni\u00e8re \u00e9tape consiste \u00e0<strong> compl\u00e9ter les informations manquantes<\/strong>. Apr\u00e8s avoir compl\u00e9t\u00e9 ce processus, les donn\u00e9es sont pr\u00eates \u00e0 \u00eatre export\u00e9es vers un catalogue de donn\u00e9es et \u00e0 \u00eatre analys\u00e9es.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-f85fc15 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"f85fc15\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-802c997\" data-id=\"802c997\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-dd4a626 elementor-widget elementor-widget-heading\" data-id=\"dd4a626\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<h3 class=\"elementor-heading-title elementor-size-default\">Comment se former au Data Cleaning ?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-a9b2390 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"a9b2390\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-fb8ff94\" data-id=\"fb8ff94\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-b57f566 elementor-widget elementor-widget-video\" data-id=\"b57f566\" data-element_type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/youtu.be\\\/SNNK6z03TaA&quot;,&quot;video_type&quot;:&quot;youtube&quot;,&quot;controls&quot;:&quot;yes&quot;}\" data-widget_type=\"video.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-wrapper elementor-open-inline\">\n\t\t\t<div class=\"elementor-video\"><\/div>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-7e26d52 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"7e26d52\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-e9db62e\" data-id=\"e9db62e\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-fe381ed elementor-widget elementor-widget-text-editor\" data-id=\"fe381ed\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Le Data Cleaning est indispensable pour la Data Science et l&rsquo;intelligence artificielle. Il est donc imp\u00e9ratif de <strong>ma\u00eetriser les diff\u00e9rents outils et techniques<\/strong> existants pour travailler dans ces domaines.<\/p><p>Pour acqu\u00e9rir ces comp\u00e9tences, vous pouvez opter pour les formations DataScientest. Nos diff\u00e9rents<strong> programmes Data Engineer, Data Analyst et Data Scientist<\/strong> vous permettent d&rsquo;apprendre \u00e0 traiter les donn\u00e9es et notamment \u00e0 les nettoyer.<\/p><p>Au terme de ces parcours professionnalisants, vous serez fin pr\u00eat \u00e0 travailler dans la Data Science. Parmi les anciens apprenants, <strong>93% ont trouv\u00e9 un emploi imm\u00e9diatement<\/strong>. Vous recevrez aussi un dipl\u00f4me certifi\u00e9 par l&rsquo;Universit\u00e9 de la Sorbonne.<\/p><p>Toutes nos formations sont propos\u00e9es <strong>en BootCamp ou en Formation Continue<\/strong>. L&rsquo;approche Blended Learning, innovante en France, concilie l&rsquo;apprentissage \u00e0 distance et en pr\u00e9sentiel pour offrir le meilleur des deux mondes. N&rsquo;attendez plus et d\u00e9couvrez <a href=\"https:\/\/datascientest.com\/formations-data\" target=\"_blank\" rel=\"noopener\">nos formations aux m\u00e9tiers de la Data Science<\/a> !<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-cf481f9 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"cf481f9\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-b0561ea\" data-id=\"b0561ea\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-a37d1f8 elementor-align-center button-principale elementor-widget elementor-widget-button\" data-id=\"a37d1f8\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm elementor-animation-grow\" href=\"https:\/\/datascientest.com\/presentation-formations\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t<span class=\"elementor-button-text\">D\u00e9couvrir les formations en Data Science <\/span>\n\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-e9c0317 elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"e9c0317\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-19b71c9\" data-id=\"19b71c9\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-ed0dcaf elementor-widget elementor-widget-text-editor\" data-id=\"ed0dcaf\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<p>Vous savez tout sur le Data Cleaning. D\u00e9couvrez <a href=\"https:\/\/datascientest.com\/data-science-definition\" target=\"_blank\" rel=\"noopener\">notre dossier complet sur la Data Science<\/a>, et <a href=\"https:\/\/datascientest.com\/machine-learning-engineer-tout-savoir\" target=\"_blank\" rel=\"noopener\">notre introduction au Machine Learning<\/a>.<\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-eef195d elementor-section-boxed elementor-section-height-default elementor-section-height-default\" data-id=\"eef195d\" data-element_type=\"section\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-8bf4b1a\" data-id=\"8bf4b1a\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-71a5022 elementor-widget elementor-widget-html\" data-id=\"71a5022\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<script type=\"application\/ld+json\">\r\n{\r\n  \"@context\": \"https:\/\/schema.org\",\r\n  \"@type\": \"FAQPage\",\r\n  \"mainEntity\": [{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"Qu'est-ce que le Data Cleaning ?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"Le Data Cleaning (aussi appel\u00e9 Data Cleansing ou Data Scrubbing) englobe plusieurs processus ayant pour but d'am\u00e9liorer la qualit\u00e9 des donn\u00e9es. Il existe de nombreux outils, et des pratiques permettant d'\u00e9liminer les probl\u00e8mes dans un dataset.\"\r\n    }\r\n  },{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"A quoi sert le Data Cleaning ?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"Les donn\u00e9es sont d\u00e9sormais une ressource essentielle pour les entreprises de tous les secteurs. A l'heure du Big Data, elles sont utilis\u00e9es comme support pour la prise de d\u00e9cisions cruciales.\"\r\n    }\r\n  },{\r\n    \"@type\": \"Question\",\r\n    \"name\": \"Les avantages du Data Cleaning\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"Le Data Cleaning offre de nombreux avantages. 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Elle consiste \u00e0 r\u00e9soudre les probl\u00e8mes dans les ensembles de donn\u00e9es, afin de pouvoir les exploiter par la suite. D\u00e9finitions, techniques, cas d&rsquo;usage, formations&#8230; Les donn\u00e9es sont indispensables en Data Science, en intelligence artificielle et en Machine [&hellip;]<\/p>\n","protected":false},"author":77,"featured_media":71153,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2497],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v21.7 (Yoast SEO v21.7) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Data Cleaning : d\u00e9finition, techniques, importance en Data Science<\/title>\n<meta name=\"description\" content=\"Le Data Cleaning ou nettoyage de donn\u00e9es est indispensable dans le domaine de la Data Science. D\u00e9couvrez tout ce qu&#039;il y a \u00e0 savoir sur ce sujet.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/datascientest.com\/data-cleaning-definition\" \/>\n<meta property=\"og:locale\" content=\"fr_FR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Data Cleaning : d\u00e9finition, techniques, importance en Data Science\" \/>\n<meta property=\"og:url\" content=\"https:\/\/datascientest.com\/data-cleaning-definition\" \/>\n<meta property=\"og:site_name\" content=\"Formation Data Science | DataScientest.com\" \/>\n<meta property=\"article:published_time\" content=\"2021-05-21T07:41:36+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-07-31T15:13:42+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/datascientest.com\/wp-content\/uploads\/2021\/05\/illu_data_cleaning_blog_2-07.png\" \/>\n\t<meta property=\"og:image:width\" content=\"1024\" \/>\n\t<meta property=\"og:image:height\" content=\"562\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/png\" \/>\n<meta name=\"author\" content=\"Raphael Kassel\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u00c9crit par\" \/>\n\t<meta name=\"twitter:data1\" content=\"Raphael Kassel\" \/>\n\t<meta name=\"twitter:label2\" content=\"Dur\u00e9e de lecture estim\u00e9e\" \/>\n\t<meta name=\"twitter:data2\" content=\"8 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/datascientest.com\/data-cleaning-definition\",\"url\":\"https:\/\/datascientest.com\/data-cleaning-definition\",\"name\":\"Data Cleaning : d\u00e9finition, techniques, importance en Data Science\",\"isPartOf\":{\"@id\":\"https:\/\/datascientest.com\/#website\"},\"datePublished\":\"2021-05-21T07:41:36+00:00\",\"dateModified\":\"2024-07-31T15:13:42+00:00\",\"author\":{\"@id\":\"https:\/\/datascientest.com\/#\/schema\/person\/6eca16f63daaafcbc96e15549b9d4ee1\"},\"description\":\"Le Data Cleaning ou nettoyage de donn\u00e9es est indispensable dans le domaine de la Data Science. 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