{"product_id":"practical-statistics-for-data-scientists-50-essential-concepts-using-r-pyt-pb","title":"Practical Statistics for Data Scientists 50+ Essential Concepts Using R \u0026 Python","description":"\u003cdiv style=\"-webkit-tap-highlight-color: rgba(0, 0, 0, 0); box-sizing: border-box; color: rgb(33, 33, 33); font-family: Arial,;\" class=\"container-fluid-row\"\u003e\n\u003cdiv style=\"-webkit-tap-highlight-color: rgba(0, 0, 0, 0); box-sizing: border-box; width: 1400px; padding-left: 20px; padding-right: 20px;\" class=\"row-fluid\"\u003e\n\u003cdiv style=\"-webkit-tap-highlight-color: rgba(0, 0, 0, 0); box-sizing: border-box; float: left; min-height: 1px; margin: 0px 0px 40px; width: 1360px; padding-bottom: 40px; padding-left: 0px; padding-right: 0px;\" class=\"span16 main-content-grid ut2-bottom\"\u003e\n\u003cdiv style=\"-webkit-tap-highlight-color: rgba(0, 0, 0, 0); box-sizing: border-box; margin: 0px; position: relative; display: table; width: 1360px; table-layout: fixed;\" class=\"ut2-pb ty-product-block ty-product-detail\"\u003e\n\u003cdiv style=\"-webkit-tap-highlight-color: rgba(0, 0, 0, 0); box-sizing: border-box; padding: 0px;\" id=\"tabs_content\" class=\"cm-tabs-content ty-tabs__content clearfix\"\u003e\n\u003cdiv style=\"-webkit-tap-highlight-color: rgba(0, 0, 0, 0); box-sizing: content-box; overflow-wrap: break-word; line-height: 1.4; position: relative; overflow: hidden; padding-top: 5px; transition: max-height 0.01s; opacity: 0.999999;\" data-ab-smc-tab-override-h=\"N\" data-ab-smc-height=\"250\" data-ab-smc-less=\"Less\" data-ab-smc-more=\"More\" data-ab-smc-tab-hide=\"N|N|Y\" class=\"ty-wysiwyg-content content-description ab-smc-description ab-smc-opened\" id=\"content_description\"\u003e\n\u003cdiv style=\"-webkit-tap-highlight-color: rgba(0, 0, 0, 0); box-sizing: border-box; overflow: hidden; padding-bottom: 42.3906px;\"\u003e\n\u003cdiv style=\"-webkit-tap-highlight-color: rgba(0, 0, 0, 0); box-sizing: border-box;\"\u003e\n\u003cp style=\"-webkit-tap-highlight-color: rgba(0, 0, 0, 0); box-sizing: border-box; margin: 0px; padding: 6px 0px;\"\u003eStatistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.\u003c\/p\u003e\n\u003cp style=\"-webkit-tap-highlight-color: rgba(0, 0, 0, 0); box-sizing: border-box; margin: 0px; padding: 6px 0px;\"\u003eMany data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you'll learn: \u003c\/p\u003e\n\u003cul style=\"-webkit-tap-highlight-color: rgba(0, 0, 0, 0); box-sizing: border-box; padding: 0px; margin: 0px 0px 0px 24px; list-style-position: initial; list-style-image: initial;\"\u003e\n\u003cli style=\"-webkit-tap-highlight-color: rgba(0, 0, 0, 0); box-sizing: border-box; margin: 0px; padding: 2px 0px; list-style-position: initial; list-style-image: initial; list-style-type: inherit;\"\u003eWhy exploratory data analysis is a key preliminary step in data science \u003c\/li\u003e\n\u003cli style=\"-webkit-tap-highlight-color: rgba(0, 0, 0, 0); box-sizing: border-box; margin: 0px; padding: 2px 0px; list-style-position: initial; list-style-image: initial; list-style-type: inherit;\"\u003eHow random sampling can reduce bias and yield a higher-quality dataset, even with big data \u003c\/li\u003e\n\u003cli style=\"-webkit-tap-highlight-color: rgba(0, 0, 0, 0); box-sizing: border-box; margin: 0px; padding: 2px 0px; list-style-position: initial; list-style-image: initial; list-style-type: inherit;\"\u003eHow the principles of experimental design yield definitive answers to questions \u003c\/li\u003e\n\u003cli style=\"-webkit-tap-highlight-color: rgba(0, 0, 0, 0); box-sizing: border-box; margin: 0px; padding: 2px 0px; list-style-position: initial; list-style-image: initial; list-style-type: inherit;\"\u003eHow to use regression to estimate outcomes and detect anomalies \u003c\/li\u003e\n\u003cli style=\"-webkit-tap-highlight-color: rgba(0, 0, 0, 0); box-sizing: border-box; margin: 0px; padding: 2px 0px; list-style-position: initial; list-style-image: initial; list-style-type: inherit;\"\u003eKey classification techniques for predicting which categories a record belongs to \u003c\/li\u003e\n\u003cli style=\"-webkit-tap-highlight-color: rgba(0, 0, 0, 0); box-sizing: border-box; margin: 0px; padding: 2px 0px; list-style-position: initial; list-style-image: initial; list-style-type: inherit;\"\u003eStatistical machine learning methods that \"learn\" from data \u003c\/li\u003e\n\u003cli style=\"-webkit-tap-highlight-color: rgba(0, 0, 0, 0); box-sizing: border-box; margin: 0px; padding: 2px 0px; list-style-position: initial; list-style-image: initial; list-style-type: inherit;\"\u003eUnsupervised learning methods for extracting meaning from unlabeled data\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv style=\"-webkit-tap-highlight-color: rgba(0, 0, 0, 0); box-sizing: border-box; overflow-wrap: break-word;\" class=\"ty-wysiwyg-content content-files\" id=\"content_files\"\u003e\u003c\/div\u003e\n\u003cdiv style=\"-webkit-tap-highlight-color: rgba(0, 0, 0, 0); box-sizing: border-box; overflow-wrap: break-word;\" class=\"ty-wysiwyg-content content-buy_together\" id=\"content_buy_together\"\u003e\u003c\/div\u003e\n\u003cdiv style=\"-webkit-tap-highlight-color: rgba(0, 0, 0, 0); box-sizing: border-box; overflow-wrap: break-word;\" class=\"ty-wysiwyg-content content-tags\" id=\"content_tags\"\u003e\u003c\/div\u003e\n\u003cdiv style=\"-webkit-tap-highlight-color: rgba(0, 0, 0, 0); box-sizing: border-box; overflow-wrap: break-word;\" class=\"ty-wysiwyg-content content-discussion\" id=\"content_discussion\"\u003e\u003c\/div\u003e\n\u003cdiv style=\"-webkit-tap-highlight-color: rgba(0, 0, 0, 0); box-sizing: border-box; overflow-wrap: break-word;\" class=\"ty-wysiwyg-content content-required_products\" id=\"content_required_products\"\u003e\u003c\/div\u003e\n\u003cdiv style=\"-webkit-tap-highlight-color: rgba(0, 0, 0, 0); box-sizing: border-box; overflow-wrap: break-word;\" class=\"ty-wysiwyg-content content-ab__video_gallery\" id=\"content_ab__video_gallery\"\u003e\u003c\/div\u003e\n\u003cdiv style=\"-webkit-tap-highlight-color: rgba(0, 0, 0, 0); box-sizing: border-box; overflow-wrap: break-word;\" class=\"ty-wysiwyg-content content-ab__deal_of_the_day\" id=\"content_ab__deal_of_the_day\"\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv style=\"-webkit-tap-highlight-color: rgba(0, 0, 0, 0); box-sizing: border-box;\" class=\"product-details\"\u003e\n\u003cp\u003eStatistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.\u003c\/p\u003e\n\u003cp\u003eMany data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you'll learn: \u003c\/p\u003e\n\u003cul\u003e\n\u003cli\u003eWhy exploratory data analysis is a key preliminary step in data science \u003c\/li\u003e\n\u003cli\u003eHow random sampling can reduce bias and yield a higher-quality dataset, even with big data \u003c\/li\u003e\n\u003cli\u003eHow the principles of experimental design yield definitive answers to questions \u003c\/li\u003e\n\u003cli\u003eHow to use regression to estimate outcomes and detect anomalies \u003c\/li\u003e\n\u003cli\u003eKey classification techniques for predicting which categories a record belongs to \u003c\/li\u003e\n\u003cli\u003eStatistical machine learning methods that \"learn\" from data \u003c\/li\u003e\n\u003cli\u003eUnsupervised learning methods for extracting meaning from unlabeled data\u003c\/li\u003e\n\u003c\/ul\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cdiv style=\"-webkit-tap-highlight-color: rgba(0, 0, 0, 0); box-sizing: border-box; color: rgb(33, 33, 33); font-family: Arial,;\" class=\"container-fluid-row container-fluid-row-full-width  b-top\"\u003e\n\u003cdiv style=\"-webkit-tap-highlight-color: rgba(0, 0, 0, 0); box-sizing: border-box; width: 1400px; padding-left: 20px; padding-right: 20px; max-width: 1400px; min-width: 280px; margin: 0px auto;\" class=\"row-fluid\"\u003e\n\u003cdiv style=\"-webkit-tap-highlight-color: rgba(0, 0, 0, 0); box-sizing: border-box; float: left; min-height: 1px; margin: 40px 0px; width: 1360px; padding-left: 0px; padding-right: 0px;\" class=\"span16 ut2-top-bottom\"\u003e\n\u003cdiv style=\"-webkit-tap-highlight-color: rgba(0, 0, 0, 0); box-sizing: border-box; width: 1360px;\" class=\"row-fluid\"\u003e\n\u003cdiv style=\"-webkit-tap-highlight-color: rgba(0, 0, 0, 0); box-sizing: border-box; float: left; min-height: 1px; margin-left: 0px; width: 1360px; padding-left: 0px; padding-right: 0px;\" class=\"span16\"\u003e\n\u003cdiv style=\"-webkit-tap-highlight-color: rgba(0, 0, 0, 0); box-sizing: border-box; margin-bottom: 0px;\" class=\"ty-mainbox-container clearfix\"\u003e\u003cbr\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e","brand":"Pearl Press","offers":[{"title":"Default Title","offer_id":45998763770053,"sku":"820326005","price":79.97,"currency_code":"AUD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0685\/7660\/8453\/files\/57_c920aa82-1a71-432f-a761-a9946e324cae.jpg?v=1774400789","url":"https:\/\/pearlpress.com.au\/products\/practical-statistics-for-data-scientists-50-essential-concepts-using-r-pyt-pb","provider":"Pearl Press","version":"1.0","type":"link"}