{"id":81805,"date":"2021-07-07T09:38:00","date_gmt":"2021-07-07T08:38:00","guid":{"rendered":"https:\/\/multi.datascientest.com\/?p=81805"},"modified":"2023-10-30T18:21:16","modified_gmt":"2023-10-30T17:21:16","slug":"hello-daniel-what-is-data-normalization","status":"publish","type":"post","link":"https:\/\/datascientest.com\/en\/hello-daniel-what-is-data-normalization","title":{"rendered":"Data normalization: How this concept is related to Data Science?"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"81805\" class=\"elementor elementor-81805\" data-elementor-post-type=\"post\">\n\t\t\t\t\t\t\t<div class=\"elementor-element elementor-element-c0eab48 e-flex e-con-boxed e-con\" data-id=\"c0eab48\" 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-f744cc9 elementor-widget elementor-widget-text-editor\" data-id=\"f744cc9\" 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><b>Daniel is the technical support of DataScientest\u2019s trainings. It is the expert on every subject related to data science. Today, we have managed to get a quick interview with him, so that he can answer a few of our questions about data normalization.<\/b><\/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-130d933 e-flex e-con-boxed e-con\" data-id=\"130d933\" 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-a9f0989 elementor-widget elementor-widget-heading\" data-id=\"a9f0989\" 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 Normalization?\n<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-30d8ccd elementor-widget elementor-widget-text-editor\" data-id=\"30d8ccd\" 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><b>Normalization<\/b><span style=\"font-weight: 400;\">, as it is heard in the data science area, is a <\/span><b>very important concept<\/b><span style=\"font-weight: 400;\"> in Data pre-processing, when you need to work on a <\/span><span style=\"text-decoration: underline;\"><a href=\"https:\/\/datascientest.com\/en\/machine-learning-what-is-it-and-why-does-it-change-the-world\"><b>Machine Learning<\/b><\/a><\/span><span style=\"font-weight: 400;\"> project.<\/span><\/p><p><span style=\"font-weight: 400;\">Two main processes are implied when we talk about normalization: <\/span><b>normalization and standard normalization<\/b><span style=\"font-weight: 400;\">, more commonly known as standardization. Generally, these two processes have the <\/span><b>same purpose<\/b><span style=\"font-weight: 400;\">: to resize numerical variables so that they are <\/span><b>comparable on a common scale<\/b><span style=\"font-weight: 400;\">.\u00a0<\/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-b050c5e e-flex e-con-boxed e-con\" data-id=\"b050c5e\" 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-1b1867b elementor-widget elementor-widget-heading\" data-id=\"1b1867b\" 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\">In mathematics terms, what do we have?\n<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-04fbed5 elementor-widget elementor-widget-text-editor\" data-id=\"04fbed5\" 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;\">Let\u2019s consider a <\/span><b>numerical variable<\/b><span style=\"font-weight: 400;\"> with n observations, than can be written as followed: <\/span><\/p>\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-604d6a5 e-flex e-con-boxed e-con\" data-id=\"604d6a5\" 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-6254ae8 elementor-widget elementor-widget-image\" data-id=\"6254ae8\" data-element_type=\"widget\" data-widget_type=\"image.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-image{text-align:center}.elementor-widget-image a{display:inline-block}.elementor-widget-image a img[src$=\".svg\"]{width:48px}.elementor-widget-image img{vertical-align:middle;display:inline-block}<\/style>\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"800\" height=\"145\" src=\"https:\/\/datascientest.com\/en\/files\/2021\/07\/With-1-1-1024x186.jpg\" class=\"attachment-large size-large wp-image-166135\" alt=\"data-normalization\" srcset=\"https:\/\/datascientest.com\/en\/files\/2021\/07\/With-1-1-1024x186.jpg 1024w, https:\/\/datascientest.com\/en\/files\/2021\/07\/With-1-1-300x55.jpg 300w, https:\/\/datascientest.com\/en\/files\/2021\/07\/With-1-1-768x140.jpg 768w, https:\/\/datascientest.com\/en\/files\/2021\/07\/With-1-1.jpg 1150w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/>\t\t\t\t\t\t\t\t\t\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-84cc24e e-flex e-con-boxed e-con\" data-id=\"84cc24e\" 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-495003f elementor-widget elementor-widget-text-editor\" data-id=\"495003f\" 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;\">As we have a finite number of real values, we can extract various statistical pieces of information, including <\/span><b>min<\/b><span style=\"font-weight: 400;\">, <\/span><b>max mean<\/b><span style=\"font-weight: 400;\">, and <\/span><b>standard deviation<\/b><span style=\"font-weight: 400;\">. The process of normalization only needs the min and max functions.<\/span><\/p><p><span style=\"font-weight: 400;\">The purpose here is to <\/span><b>bring back all the values of the variable between 0 and 1<\/b><span style=\"font-weight: 400;\"> while keeping some distance between the values.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">To do that, you\u2019ll use a simple formula:<\/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\t\t<div class=\"elementor-element elementor-element-abc9ec2 elementor-widget elementor-widget-image\" data-id=\"abc9ec2\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"429\" height=\"109\" src=\"https:\/\/datascientest.com\/en\/files\/2021\/07\/formula-2.jpg\" class=\"attachment-large size-large wp-image-166139\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2021\/07\/formula-2.jpg 429w, https:\/\/datascientest.com\/en\/files\/2021\/07\/formula-2-300x76.jpg 300w\" sizes=\"(max-width: 429px) 100vw, 429px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c35cddc elementor-widget elementor-widget-text-editor\" data-id=\"c35cddc\" 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;\">Regarding the <\/span><b>standardization<\/b><span style=\"font-weight: 400;\">, the transformation is more diffiuclt than easily bringing back the values between 0 and 1. It aims at bringing back <\/span><b>the average \u03bc to 0<\/b><span style=\"font-weight: 400;\"> and the <\/span><b>standard deviation to 1<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">Again, the process is not very complicated: if you already know the <\/span><b>mean \u03bc<\/b><span style=\"font-weight: 400;\"> and the <\/span><b>standard deviation \u03c3<\/b><span style=\"font-weight: 400;\"> of a variable X =\u00a0x<sub>1<\/sub>\u00a0x<sub>2<\/sub>\u00a0x<sub>n<\/sub> you will write the <\/span><b>standardized variable<\/b><span style=\"font-weight: 400;\"> as followed:<\/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-c09cb29 elementor-widget elementor-widget-image\" data-id=\"c09cb29\" data-element_type=\"widget\" data-widget_type=\"image.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<img decoding=\"async\" width=\"434\" height=\"122\" src=\"https:\/\/datascientest.com\/en\/files\/2021\/07\/formula-3.jpg\" class=\"attachment-large size-large wp-image-166140\" alt=\"\" srcset=\"https:\/\/datascientest.com\/en\/files\/2021\/07\/formula-3.jpg 434w, https:\/\/datascientest.com\/en\/files\/2021\/07\/formula-3-300x84.jpg 300w\" sizes=\"(max-width: 434px) 100vw, 434px\" \/>\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2bd434b elementor-widget elementor-widget-spacer\" data-id=\"2bd434b\" 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-fa49d77 e-flex e-con-boxed e-con\" data-id=\"fa49d77\" 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-79b5be2 elementor-widget elementor-widget-heading\" data-id=\"79b5be2\" 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 link between Data Normalization and Data Science?\n<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f6d53dc elementor-widget elementor-widget-text-editor\" data-id=\"f6d53dc\" 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;\">In <\/span><span style=\"text-decoration: underline;\"><a href=\"https:\/\/datascientest.com\/en\/data-science-definition-issues-and-use-cases\"><b>Data Science<\/b><\/a><\/span><span style=\"font-weight: 400;\">, you\u2019re often dealing with <\/span><b>numerical data<\/b><span style=\"font-weight: 400;\">, and you can rarely compare these data in their <\/span><b>original state<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">Working with variable scale data <\/span><b>can be a problem<\/b><span style=\"font-weight: 400;\"> in analysis because a numerical variable with a <\/span><b>range of values between 0 and 10,000<\/b><span style=\"font-weight: 400;\"> will be more important in the analysis than a variable with values between <\/span><b>0 and 1<\/b><span style=\"font-weight: 400;\">, which would cause a<\/span><b> bias problem<\/b><span style=\"font-weight: 400;\"> later on.<\/span><\/p><p><span style=\"font-weight: 400;\">However, be careful not to consider normalization as a mandatory step in processing Data, it constitutes a<\/span><b> loss of information in the short term<\/b><span style=\"font-weight: 400;\"> and can be <\/span><b>detrimental<\/b><span style=\"font-weight: 400;\"> in certain cases!<\/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-b2f74a5 elementor-widget elementor-widget-spacer\" data-id=\"b2f74a5\" 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-78de230 e-flex e-con-boxed e-con\" data-id=\"78de230\" 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-5e7b56d elementor-widget elementor-widget-heading\" data-id=\"5e7b56d\" 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 do you normalize data concretely?\n<\/h3>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4c5e52d elementor-widget elementor-widget-text-editor\" data-id=\"4c5e52d\" 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;\">With <\/span><span style=\"text-decoration: underline;\"><strong><a href=\"https:\/\/datascientest.com\/en\/python-the-most-popular-programming-language\">Python<\/a><\/strong><\/span><span style=\"font-weight: 400;\"> it is very simple, many libraries allow it. I will only mention <\/span><b>Scikit-learn<\/b><span style=\"font-weight: 400;\"> because it is the <\/span><b>most used in Data Science<\/b><span style=\"font-weight: 400;\">. This library offers functions that perform the desired normalizations in a few simple lines of code.<\/span><\/p><p><span style=\"font-weight: 400;\">However, it is important to put the <\/span><b>use cases<\/b><span style=\"font-weight: 400;\"> in context, because in practice it is not enough to apply a silly normalization to all the Data we have when we already normalized our <\/span><b>training data<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">Why not? The reason is very simple: It is not possible to apply this same transformation to a <\/span><b>test sample, or new data<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">It is obviously possible to <\/span><b>center and reduce<\/b><span style=\"font-weight: 400;\"> any sample in the same way, but with an average and standard deviation that will be <\/span><b>different from those used on the training set<\/b><span style=\"font-weight: 400;\">.<\/span><\/p><p><span style=\"font-weight: 400;\">The results obtained would not be a fair representation of the <\/span><b>performance<\/b><span style=\"font-weight: 400;\"> of the model, when applied to new data.<\/span><\/p><p><span style=\"font-weight: 400;\">So, rather than applying the normalization function directly, it is better to use a <\/span><b>Scikit-Learn feature called transformer API<\/b><span style=\"font-weight: 400;\">, which will allow you to adjust (<\/span><b><i>fit<\/i><\/b><span style=\"font-weight: 400;\">)<\/span> <span style=\"font-weight: 400;\">a preprocessing step using the training data.<\/span><\/p><p><span style=\"font-weight: 400;\">So when <\/span><b>normalization<\/b><span style=\"font-weight: 400;\">, for example, is applied to other samples, it will use the same saved average and standard deviations.\u00a0<\/span><\/p><p><span style=\"font-weight: 400;\">To create this \u2018<\/span><b>adjusted<\/b><span style=\"font-weight: 400;\">\u2019 preprocessing step, simply use the \u2018<\/span><b><i>StandardScaler<\/i><\/b><span style=\"font-weight: 400;\">\u2019 function and adjust it using the training data. Finally, to apply it to an array of data afterward, simply apply the following formula: <\/span><b><i>scaler.transform()<\/i><\/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-311b186 elementor-widget elementor-widget-html\" data-id=\"311b186\" data-element_type=\"widget\" data-widget_type=\"html.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t<script src=\"https:\/\/gist.github.com\/AdrienRuimy\/c24d138fe3403be1eafa4e608e9e26f8.js\"><\/script>\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-fb80b9d elementor-widget elementor-widget-spacer\" data-id=\"fb80b9d\" 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-e6eb135 e-flex e-con-boxed e-con\" data-id=\"e6eb135\" 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-0664ce8 elementor-align-center button-principal elementor-widget elementor-widget-button\" data-id=\"0664ce8\" 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-courses\">\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 differents Data Science's courses<\/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\t\t<\/div>\n\t\t<div class=\"elementor-element elementor-element-0a36d4a e-flex e-con-boxed e-con\" data-id=\"0a36d4a\" 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-b1aa505 elementor-widget elementor-widget-html\" data-id=\"b1aa505\" 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 Normalization?\",\r\n    \"acceptedAnswer\": {\r\n      \"@type\": \"Answer\",\r\n      \"text\": \"Two main processes are implied when we talk about normalization: normalization and standard normalization, more commonly known as standardization. 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Normalization, as it is heard in the data [&hellip;]<\/p>\n","protected":false},"author":45,"featured_media":30548,"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 normalization: How this concept is related to Data Science?<\/title>\n<meta name=\"description\" content=\"Daniel is the technical support of DataScientest\u2019s trainings. It is the expert on every subject related to data science, and which is now walking every of our learners through their trainings. 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