{"id":167727,"date":"2023-04-20T13:28:02","date_gmt":"2023-04-20T12:28:02","guid":{"rendered":"https:\/\/datascientest.com\/en\/?p=167727"},"modified":"2023-10-30T18:50:23","modified_gmt":"2023-10-30T17:50:23","slug":"data-cleaning-definition-methods-and-relevance-in-data-science","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/data-cleaning-definition-methods-and-relevance-in-data-science","title":{"rendered":"Data cleaning : Definition, methods and relevance in Data Science"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"167727\" class=\"elementor elementor-167727\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-827411f e-flex e-con-boxed e-con\" data-id=\"827411f\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-386dc07 elementor-widget elementor-widget-text-editor\" data-id=\"386dc07\" 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><span style=\"font-weight: 400;\">Data cleaning is an essential step in Data Science and Machine Learning. It consists in solving problems in data sets, to be able to exploit them later on. Definitions, techniques, use cases, training&#8230;<\/span><\/p><p><b>Data is essential<\/b><span style=\"font-weight: 400;\"> in Data Science, Artificial Intelligence, and Machine Learning. They are the fuel of these technologies.<\/span><\/p><p><span style=\"font-weight: 400;\">Therefore, it is very important to ensure data quality. Today, it is very easy to find good quality, clean and structured data on dedicated marketplaces. On the other hand, for a company to clean its <\/span><b>internal data<\/b><span style=\"font-weight: 400;\">, a company must resort to data cleaning.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8f02791 elementor-widget__width-initial elementor-widget elementor-widget-video\" data-id=\"8f02791\" data-element_type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/www.youtube.com\\\/watch?v=qc4oWfpjnio&amp;list=PLbH8UGHWFlsTo6cG8Fgbgz0MWT7X4TGTW&amp;index=4&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<div class=\"elementor-element elementor-element-3f7746a elementor-widget elementor-widget-spacer\" data-id=\"3f7746a\" data-element_type=\"widget\" data-widget_type=\"spacer.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-column .elementor-spacer-inner{height:var(--spacer-size)}.e-con{--container-widget-width:100%}.e-con-inner>.elementor-widget-spacer,.e-con>.elementor-widget-spacer{width:var(--container-widget-width,var(--spacer-size));--align-self:var(--container-widget-align-self,initial);--flex-shrink:0}.e-con-inner>.elementor-widget-spacer>.elementor-widget-container,.e-con-inner>.elementor-widget-spacer>.elementor-widget-container>.elementor-spacer,.e-con>.elementor-widget-spacer>.elementor-widget-container,.e-con>.elementor-widget-spacer>.elementor-widget-container>.elementor-spacer{height:100%}.e-con-inner>.elementor-widget-spacer>.elementor-widget-container>.elementor-spacer>.elementor-spacer-inner,.e-con>.elementor-widget-spacer>.elementor-widget-container>.elementor-spacer>.elementor-spacer-inner{height:var(--container-widget-height,var(--spacer-size))}<\/style>\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\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\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-c8a4868 e-flex e-con-boxed e-con\" data-id=\"c8a4868\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-02c948b elementor-widget elementor-widget-heading\" data-id=\"02c948b\" 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><h3 class=\"elementor-heading-title elementor-size-default\">What is Data Cleaning?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6f0617e elementor-widget elementor-widget-text-editor\" data-id=\"6f0617e\" 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><span style=\"font-weight: 400;\">Data Cleaning encompasses several processes to <\/span><b>improve data quality<\/b><span style=\"font-weight: 400;\">. There are many tools and practices to eliminate problems in a dataset.<\/span><\/p><p><span style=\"font-weight: 400;\">These processes are used to correct or remove inaccurate records in a database or dataset. Generally speaking, this means <\/span><b>identifying and replacing<\/b><span style=\"font-weight: 400;\"> incomplete, inaccurate, corrupt, or irrelevant data or records.<\/span><\/p><p><span style=\"font-weight: 400;\">After a properly performed data cleaning, all data sets should be <\/span><b>consistent and error-free<\/b><span style=\"font-weight: 400;\">. This is essential for the use and exploitation of the data.<\/span><\/p><p><span style=\"font-weight: 400;\">Without cleaning, the results of the analyses are likely to be skewed. Similarly, a Machine Learning or AI model trained on bad data can be biased or deliver poor performance.<\/span><\/p><p><span style=\"font-weight: 400;\">Data Cleaning is different from Data Transformation. Cleaning is about <\/span><b>converting data from one format to another<\/b><span style=\"font-weight: 400;\">, while Transformation (also called Wrangling) is about <\/span><b>converting the raw data into a format suitable for analysis<\/b><span style=\"font-weight: 400;\">.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-edac1bc elementor-widget elementor-widget-spacer\" data-id=\"edac1bc\" data-element_type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\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\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-31c47fe e-flex e-con-boxed e-con\" data-id=\"31c47fe\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-8d9ca92 elementor-widget elementor-widget-heading\" data-id=\"8d9ca92\" 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\">What is the purpose of data cleaning?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ce1132e elementor-widget elementor-widget-text-editor\" data-id=\"ce1132e\" 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><span style=\"font-weight: 400;\">Data is now an essential resource for companies in all sectors. In the age of Big Data, it is used to support <\/span><b>critical decision-making<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">According to a study conducted by IBM, poor data quality now costs $3.1 trillion per year in the United States. And that cost is <\/span><b>growing exponentially<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">Prevention through data cleansing is relatively affordable, but fixing existing problems can cost ten times as much. Even worse, fixing a problem in the data after it has caused an outage is <\/span><b>100 times more expensive<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">A wide variety of problems can arise from<\/span><b> low-quality data<\/b><span style=\"font-weight: 400;\">. For example, a marketing campaign may be poorly targeted and therefore fail.<\/span><\/p><p><span style=\"font-weight: 400;\">In the <\/span><b>medical branch,<\/b><span style=\"font-weight: 400;\"> poor data can lead to inappropriate treatments and even to the failure of drug development. A study by Accenture reveals that a lack of clean data is the biggest barrier to AI adoption in this field.<\/span><\/p><p><span style=\"font-weight: 400;\">In <\/span><b>logistics<\/b><span style=\"font-weight: 400;\">, data can cause problems with inventory, delivery planning, and thus affect customer satisfaction. In the manufacturing field, factories configuring robots with bad data are exposed to serious problems.<\/span><\/p><p><span style=\"font-weight: 400;\">Finally, data cleaning is required to comply with privacy regulations imposed by laws. Regardless of the sector, this practice can therefore avoid major problems.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-174688a elementor-widget elementor-widget-spacer\" data-id=\"174688a\" data-element_type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\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\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-1ea6d6f e-flex e-con-boxed e-con\" data-id=\"1ea6d6f\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-6231e4d elementor-widget elementor-widget-heading\" data-id=\"6231e4d\" 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\">The advantages of data cleaning<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-833dc3f elementor-widget elementor-widget-text-editor\" data-id=\"833dc3f\" 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><span style=\"font-weight: 400;\">Data cleaning offers many benefits. One of the main benefits is to enable better <\/span><a href=\"https:\/\/datascientest.com\/en\/data-driven-definition-benefits-and-methods\"><b>data-driven decision-making<\/b><\/a><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">Higher quality positively impacts all activities involving data. Data is becoming increasingly important in all sectors.<\/span><\/p><p><span style=\"font-weight: 400;\">To take full advantage of this practice, <\/span><b>data cleaning<\/b><span style=\"font-weight: 400;\"> must be seen as an enterprise-wide effort. It not only streamlines business operations but also increases productivity as teams no longer have to waste time on incorrect data.<\/span><\/p><p><span style=\"font-weight: 400;\">Sales can increase if marketing teams have access to the best data. The accumulation of these <\/span><b>various internal and external benefits<\/b><span style=\"font-weight: 400;\"> leads to increased profitability.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2bc4960 elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"2bc4960\" 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-md\" href=\"https:\/\/datascientest.com\/en\/data-management-course\">\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\">Start a Data Cleaning training course<\/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<div class=\"elementor-element elementor-element-34d78e6 elementor-widget elementor-widget-spacer\" data-id=\"34d78e6\" data-element_type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\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\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-c9b7398 e-flex e-con-boxed e-con\" data-id=\"c9b7398\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-3949efd elementor-widget elementor-widget-heading\" data-id=\"3949efd\" 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\">The different types of data problems<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a7c990a elementor-widget elementor-widget-text-editor\" data-id=\"a7c990a\" 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><span style=\"font-weight: 400;\">Companies collect a wide variety of data, from <\/span><b>multiple sources<\/b><span style=\"font-weight: 400;\">. This information can be collected internally or from customers, or even captured from the web and social networks.<\/span><\/p><p><span style=\"font-weight: 400;\">However, during this process, different problems can arise. First of all, a dataset can contain duplicate data, i.e. several identical records.<\/span><\/p><p><span style=\"font-weight: 400;\">Data can also conflict. A dataset may contain several similar records with different attributes.<\/span><\/p><p><span style=\"font-weight: 400;\">On the contrary, sometimes <\/span><b>data attributes<\/b><span style=\"font-weight: 400;\"> are missing. The data may also not be compliant with regulations.<\/span><\/p><p><span style=\"font-weight: 400;\">These problems can be caused by different sources. It may be a synchronization issue, where data is not properly shared between two systems.<\/span><\/p><p><span style=\"font-weight: 400;\">Another cause can be a software bug in <\/span><b>data processing applications<\/b><span style=\"font-weight: 400;\">. Information may be &#8220;written&#8221; with errors, while correct data may be overwritten by accident.<\/span><\/p><p><span style=\"font-weight: 400;\">Finally, the cause may simply be human. Consumers may deliberately provide incomplete or incorrect data to protect their privacy.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-28ec67c elementor-widget__width-initial elementor-widget elementor-widget-video\" data-id=\"28ec67c\" data-element_type=\"widget\" data-settings=\"{&quot;youtube_url&quot;:&quot;https:\\\/\\\/www.youtube.com\\\/watch?v=kNPe_pgbuHg&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<div class=\"elementor-element elementor-element-c4a93a4 elementor-widget elementor-widget-spacer\" data-id=\"c4a93a4\" data-element_type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\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\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-d676e2c e-flex e-con-boxed e-con\" data-id=\"d676e2c\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-bf28d72 elementor-widget elementor-widget-heading\" data-id=\"bf28d72\" 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\">What are the characteristics of high-quality data?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-df43ab6 elementor-widget elementor-widget-text-editor\" data-id=\"df43ab6\" 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><span style=\"font-weight: 400;\">To be considered high quality, data <\/span><b>must meet several criteria<\/b><span style=\"font-weight: 400;\">. It must be &#8220;valid&#8221;, which means that it corresponds to the rules and constraints set by the company. This may include constraints on data types, values, or the organization of data in databases.<\/span><\/p><p><span style=\"font-weight: 400;\">Quality data must also be accurate, complete, consistent, uniform, and traceable. These are the characteristics that impact data quality and that can be corrected with data cleaning.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2d3d6e9 elementor-widget elementor-widget-spacer\" data-id=\"2d3d6e9\" data-element_type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\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\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-0853101 e-flex e-con-boxed e-con\" data-id=\"0853101\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-658fab5 elementor-widget elementor-widget-heading\" data-id=\"658fab5\" 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\">The steps of data cleaning<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-034ca2d elementor-widget elementor-widget-text-editor\" data-id=\"034ca2d\" 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><span style=\"font-weight: 400;\">To be effective, data cleaning must be considered as a <\/span><b>step-by-step process<\/b><span style=\"font-weight: 400;\">. To begin, a data quality plan must be established.<\/span><\/p><p><span style=\"font-weight: 400;\">This plan consists of identifying the main source of errors and problems, and determining how to remedy them. Corrective actions should be distributed to the appropriate managers.<\/span><\/p><p><span style=\"font-weight: 400;\">In addition, metrics should be chosen to <\/span><b>measure data quality <\/b><span style=\"font-weight: 400;\">in a clear and concise manner. This will subsequently help prioritize data-cleaning initiatives.<\/span><\/p><p><span style=\"font-weight: 400;\">Finally, a set of actions and steps to be taken must be identified to start the process. These actions will be <\/span><b>updated<\/b><span style=\"font-weight: 400;\"> over time as the data quality changes and the business evolves.<\/span><\/p><p><span style=\"font-weight: 400;\">The <\/span><b>second step<\/b><span style=\"font-weight: 400;\"> is to correct the data at the source before it enters the system in the wrong form. This practice <\/span><b>saves time and energy<\/b><span style=\"font-weight: 400;\"> and allows problems to be corrected before it is too late.<\/span><\/p><p><span style=\"font-weight: 400;\">After that, it is important to measure the accuracy of the data in real-time. There are various tools and techniques available for this purpose.<\/span><\/p><p><span style=\"font-weight: 400;\">If you, unfortunately, cannot remove duplicates at the source, it is important to <\/span><b>detect <\/b><span style=\"font-weight: 400;\">and actively remove them afterward. You should also standardize, normalize, merge, aggregate and filter the data.<\/span><\/p><p><span style=\"font-weight: 400;\">Finally, the<\/span><b> last step<\/b><span style=\"font-weight: 400;\"> is to complete the missing information. After completing this process, the data is <\/span><b>ready to be exported<\/b><span style=\"font-weight: 400;\"> to a data catalog and analyzed.<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-31daec2 elementor-widget elementor-widget-spacer\" data-id=\"31daec2\" data-element_type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\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\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-ba98198 e-flex e-con-boxed e-con\" data-id=\"ba98198\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-f58478d elementor-widget elementor-widget-heading\" data-id=\"f58478d\" 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\">How to get trained in Data Cleaning?<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a20455d elementor-widget elementor-widget-text-editor\" data-id=\"a20455d\" 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><span style=\"font-weight: 400;\">Data Cleaning is <\/span><b>essential for Data Science<\/b><span style=\"font-weight: 400;\"> and\u00a0<\/span><a href=\"https:\/\/datascientest.com\/en\/artificial-intelligence-definition\" target=\"_blank\" rel=\"noopener\"><b>Artificial Intelligence<\/b><\/a><span style=\"font-weight: 400;\">. It is therefore imperative to master the various tools and techniques that exist to work in these fields.<\/span><\/p><p><span style=\"font-weight: 400;\">To acquire these skills, you can opt for DataScientest training. Our different programs <\/span><a href=\"https:\/\/datascientest.com\/en\/data-engineer-course\"><b>Data Engineer<\/b><\/a><span style=\"font-weight: 400;\">, <\/span><a href=\"https:\/\/datascientest.com\/en\/data-analyst-course\"><b>Data Analyst<\/b><\/a><span style=\"font-weight: 400;\">, and <\/span><a href=\"https:\/\/datascientest.com\/en\/data-scientist-course\"><b>Data Scientist<\/b><\/a><span style=\"font-weight: 400;\"> allow you to learn how to process data and especially how to clean it.<\/span><\/p><p><span style=\"font-weight: 400;\">At the end of these professionalizing courses, you will be ready to work in<\/span><b> Data Science<\/b><span style=\"font-weight: 400;\">. Among former students, 93% found a job immediately. You will also receive a <\/span><b>degree certified by the Sorbonne University<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">All our courses are offered as BootCamp or Continuing Education. The<\/span><b> Blended Learning approach<\/b><span style=\"font-weight: 400;\">, innovative in France, reconciles distance and face-to-face learning to offer the best of both worlds. Don&#8217;t wait any longer and discover our Data Science training courses!<\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-76cda6b elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"76cda6b\" 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-md\" href=\"https:\/\/datascientest.com\/en\/data-management-course\">\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\">Discover our Data Management course<\/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<div class=\"elementor-element elementor-element-3c2ca37 elementor-widget elementor-widget-text-editor\" data-id=\"3c2ca37\" 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><span style=\"font-weight: 400;\">Now that you know everything on Data cleaning, discover our blog post on <\/span><strong><a href=\"https:\/\/datascientest.com\/en\/data-science-definition-issues-and-use-cases\">Data Science<\/a><\/strong><span style=\"font-weight: 400;\"> or the one on the <\/span><strong><a href=\"https:\/\/datascientest.com\/en\/machine-learning-what-is-it-and-why-does-it-change-the-world\">main concepts of Machine Learning<\/a><\/strong><span style=\"font-weight: 400;\">. <\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-1777d4c e-flex e-con-boxed e-con\" data-id=\"1777d4c\" data-element_type=\"container\" data-settings=\"{&quot;content_width&quot;:&quot;boxed&quot;}\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-8d0afba elementor-widget elementor-widget-html\" data-id=\"8d0afba\" 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\": \"What is Data Cleaning?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"Data Cleaning encompasses several processes to improve data quality. 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It consists in solving problems in data sets, to be able to exploit them later on. Definitions, techniques, use cases, training&#8230; Data is essential in Data Science, Artificial Intelligence, and Machine Learning. They are the fuel of these technologies. Therefore, it is very [&hellip;]<\/p>\n","protected":false},"author":45,"featured_media":167728,"comment_status":"open","ping_status":"open","sticky":false,"template":"elementor_theme","format":"standard","meta":{"content-type":"","inline_featured_image":false},"categories":[2425],"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 : Definition, methods and relevance in Data Science<\/title>\n<meta name=\"description\" content=\"Data cleaning is an essential step in Data Science and Machine Learning. 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