{"id":25560341,"date":"2023-05-18T15:24:03","date_gmt":"2023-05-18T09:54:03","guid":{"rendered":"https:\/\/entri.app\/blog\/?p=25560341"},"modified":"2023-05-31T15:04:37","modified_gmt":"2023-05-31T09:34:37","slug":"basic-statistics-to-start-data-science-journey","status":"publish","type":"post","link":"https:\/\/entri.app\/blog\/basic-statistics-to-start-data-science-journey\/","title":{"rendered":"Basic Statistics to Start Data Science Journey"},"content":{"rendered":"<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_79_2 counter-hierarchy ez-toc-counter ez-toc-custom ez-toc-container-direction\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<label for=\"ez-toc-cssicon-toggle-item-69d324fa24136\" class=\"ez-toc-cssicon-toggle-label\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/label><input type=\"checkbox\"  id=\"ez-toc-cssicon-toggle-item-69d324fa24136\"  aria-label=\"Toggle\" \/><nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/entri.app\/blog\/basic-statistics-to-start-data-science-journey\/#Data_Types\" >Data Types<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/entri.app\/blog\/basic-statistics-to-start-data-science-journey\/#The_Four_Levels_of_Data\" >The Four Levels of Data<\/a><\/li><\/ul><\/nav><\/div>\n<p>A crucial part of data analytics and machine learning is statistics. Finding hidden patterns through data analysis and visualization is made easier. Learning programming and statistics together should be your first step if you are interested in machine learning and want to advance your career in this field. You can discover basic statistics for machine learning principles in this article. We will cover important data types and major four levels of data to learn before getting deep into the data science field.<\/p>\n<p style=\"text-align: center;\"><strong><a href=\"https:\/\/entri.app\/course\/data-science-and-machine-learning-course\/\" target=\"_blank\" rel=\"noopener\">Start your Data Science Journey with Expert Mentors! Get a free Demo Here!<\/a><\/strong><\/p>\n<h2><span class=\"ez-toc-section\" id=\"Data_Types\"><\/span><strong>Data Types <\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>A data type is a classification that specifies which type of value a variable has in programming.\u00a0 The kinds of operations (mathematical, logical, etc.) that can be carried out without producing errors are also specified by a data type. There are essentially two forms of data:<\/p>\n<p><strong>Quantitative as well as qualitative<\/strong><\/p>\n<h4><strong>\u00a0<\/strong><strong>Qualitative Data:<\/strong><\/h4>\n<p>The definition of qualitative data states that it can be subjectively observed. In other words, it relates to the qualities and ways in which an object can be described, including its scent, form, texture, and colour. You work with qualitative data while categorizing anything based on its features. Both of these are categorical and numerical.<\/p>\n<h4><strong>Quantitative Data: <\/strong><\/h4>\n<p>Quantitative data is information that can be measured using numbers. A numerical value can be assigned to an object if its dimensions, such as its length, width, and height, are measured. You are therefore working with a quantitative data set.<\/p>\n<p>You can further separate quantitative data into two categories: discontinuous and discrete.<\/p>\n<p>When the count is more specific and includes integers, we refer to the data as being<strong> discrete<\/strong>. The number of students in a class is discrete data because it will always be a whole number, which will help you understand. It will therefore always be a 30, 56, etc.<\/p>\n<p><strong>Continuous<\/strong> data can be split up or decreased to produce better or more precise findings. We can measure an object&#8217;s height to obtain more accurate scales, such as meters, centimeters, millimeters, and so forth, for better understanding.<\/p>\n<h2><span class=\"ez-toc-section\" id=\"The_Four_Levels_of_Data\"><\/span><strong>The Four Levels of Data<\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n<p>The four different levels of measurements are listed in descending order of precision as follows:<\/p>\n<h4><strong>Quantitative Level<\/strong><\/h4>\n<h5><strong>Nominal <\/strong><\/h5>\n<p>The first level of measurement is referred to as the nominal level.The variable&#8217;s numbers are only utilized at this level to categorize the data. The use of words, letters, and alphanumeric symbols is permitted at this level of measurement. The person belonging to the female gender might be classified as F, the person belonging to the male gender could be classified as M, and transgender people could be classified as T in the event where there exist data regarding people belonging to three separate gender categories. The nominal level of measurement is this form of classification for assigning.<\/p>\n<p style=\"text-align: center;\"><strong><a href=\"https:\/\/entri.app\/course\/data-science-and-machine-learning-course\/\" target=\"_blank\" rel=\"noopener\">Looking for a Data Science Career? Explore Here!<\/a><\/strong><\/p>\n<h5><strong>\u00a0<\/strong><strong>Ordinal<\/strong><\/h5>\n<p>This is the second level of measurement, showing an ordered relationship between the observed variables. If a student receives the class&#8217;s highest grade of 100, he or she will be given the first rank. The second student, who had the second-highest score of 92, was given the second position, followed by the third student, who received the third-highest score of 89. An ordering of the measures is indicated by the ordinal level of measurement.<\/p>\n<h4><strong>Quantitative Level<\/strong><\/h4>\n<h5><strong>Nominal<\/strong><\/h5>\n<p>The distance between each interval on the scalar equivalent along the scale from low interval to high interval is specified. For instance, an interval level measurement could be the measurement of a student&#8217;s anxiety between a score of 10 and 11. This range applies to students who score between 40 and 41 as well.<\/p>\n<h5><strong>Ratio<\/strong><\/h5>\n<p>In this level of measurement, the observation can have a value of zero in addition to equal intervals. This scale distinguishes these measurements from others of its kind. Even though the characteristics resemble those of an interval level of measurement. The division between the scale&#8217;s points has an equal distance between them in the ratio measurement.<\/p>\n<p>For instance, tally, weight, height, etc.<\/p>\n<p style=\"text-align: center;\"><strong><a href=\"https:\/\/entri.app\/course\/data-science-and-machine-learning-course\/\" target=\"_blank\" rel=\"noopener\">Start your Data Science Journey with Expert Mentors! Get a free Demo Here!<\/a><\/strong><\/p>\n<p><strong>Wrap up<\/strong><\/p>\n<p>The basic of data science is statistics. Hence if you are interested to start your data science journey, you need some important statistical understanding. With this blog we have covered a minimal but powerful statistical topic such as data types and its levels. With our other articles, we will be covering all other important statistical measurements that you need to understand on your machine learning path.<\/p>\n<table dir=\"ltr\" border=\"1\" cellspacing=\"0\" cellpadding=\"0\">\n<colgroup>\n<col width=\"448\" \/>\n<col width=\"471\" \/><\/colgroup>\n<tbody>\n<tr>\n<td style=\"text-align: center;\" colspan=\"2\" rowspan=\"1\" data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;Related Links &quot;}\"><strong>Related Links<\/strong><\/td>\n<\/tr>\n<tr>\n<td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;What Is Data Mining?&quot;}\" data-sheets-hyperlink=\"https:\/\/entri.app\/blog\/what-is-data-mining-a-complete-guide\/\">\n<p class=\"jeg_post_title\"><strong><a href=\"https:\/\/entri.app\/blog\/random-variables-and-probability-distribution\/\" target=\"_blank\" rel=\"noopener\">Random Variables and Probability Distribution<\/a><\/strong><\/p>\n<\/td>\n<td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;What is Data Interpretation? Methods and Benefits&quot;}\" data-sheets-hyperlink=\"https:\/\/entri.app\/blog\/what-is-data-interpretation-methods-and-benefits\/\"><strong><a class=\"in-cell-link\" href=\"https:\/\/entri.app\/blog\/what-is-data-interpretation-methods-and-benefits\/\" target=\"_blank\" rel=\"noopener\">What is Data Interpretation? Methods and Benefits<\/a><\/strong><\/td>\n<\/tr>\n<tr>\n<td data-sheets-value=\"{&quot;1&quot;:2,&quot;2&quot;:&quot;What is Data Interpretation? Methods and Benefits&quot;}\" data-sheets-hyperlink=\"https:\/\/entri.app\/blog\/what-is-data-interpretation-methods-and-benefits\/\"><strong><a class=\"in-cell-link\" href=\"https:\/\/entri.app\/blog\/what-is-data-interpretation-methods-and-benefits\/\" target=\"_blank\" rel=\"noopener\">What is Data Interpretation? 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Finding hidden patterns through data analysis and visualization is made easier. Learning programming and statistics together should be your first step if you are interested in machine learning and want to advance your career in this field. You can discover basic statistics for machine [&hellip;]<\/p>\n","protected":false},"author":119,"featured_media":25560343,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"categories":[802,1864,1841],"tags":[],"class_list":["post-25560341","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-articles","category-data-science-ml","category-entri-skilling"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Basic Statistics to Start Data Science Journey - Entri Blog<\/title>\n<meta name=\"description\" content=\"Discover the fundamentals of basic statistics and kickstart your data science journey. 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