{"version":"1.0","provider_name":"insideBIGDATA","provider_url":"https:\/\/insidebigdata.com","author_name":"Editorial Team","author_url":"https:\/\/insidebigdata.com\/author\/editorial\/","title":"A Data Scientist's Guide to Communicating Results - insideBIGDATA","type":"rich","width":600,"height":338,"html":"<blockquote class=\"wp-embedded-content\" data-secret=\"HtxteHCtKj\"><a href=\"https:\/\/insidebigdata.com\/2018\/03\/28\/data-scientists-guide-communicating-results\/\">A Data Scientist&#8217;s Guide to Communicating Results<\/a><\/blockquote><iframe sandbox=\"allow-scripts\" security=\"restricted\" src=\"https:\/\/insidebigdata.com\/2018\/03\/28\/data-scientists-guide-communicating-results\/embed\/#?secret=HtxteHCtKj\" width=\"600\" height=\"338\" title=\"&#8220;A Data Scientist&#8217;s Guide to Communicating Results&#8221; &#8212; insideBIGDATA\" data-secret=\"HtxteHCtKj\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\" class=\"wp-embedded-content\"><\/iframe><script type=\"text\/javascript\">\n\/*! This file is auto-generated *\/\n!function(c,d){\"use strict\";var e=!1,o=!1;if(d.querySelector)if(c.addEventListener)e=!0;if(c.wp=c.wp||{},c.wp.receiveEmbedMessage);else if(c.wp.receiveEmbedMessage=function(e){var t=e.data;if(!t);else if(!(t.secret||t.message||t.value));else if(\/[^a-zA-Z0-9]\/.test(t.secret));else{for(var r,s,a,i=d.querySelectorAll('iframe[data-secret=\"'+t.secret+'\"]'),n=d.querySelectorAll('blockquote[data-secret=\"'+t.secret+'\"]'),o=new RegExp(\"^https?:$\",\"i\"),l=0;l<n.length;l++)n[l].style.display=\"none\";for(l=0;l<i.length;l++)if(r=i[l],e.source!==r.contentWindow);else{if(r.removeAttribute(\"style\"),\"height\"===t.message){if(1e3<(s=parseInt(t.value,10)))s=1e3;else if(~~s<200)s=200;r.height=s}if(\"link\"===t.message)if(s=d.createElement(\"a\"),a=d.createElement(\"a\"),s.href=r.getAttribute(\"src\"),a.href=t.value,!o.test(a.protocol));else if(a.host===s.host)if(d.activeElement===r)c.top.location.href=t.value}}},e)c.addEventListener(\"message\",c.wp.receiveEmbedMessage,!1),d.addEventListener(\"DOMContentLoaded\",t,!1),c.addEventListener(\"load\",t,!1);function t(){if(o);else{o=!0;for(var e,t,r,s=-1!==navigator.appVersion.indexOf(\"MSIE 10\"),a=!!navigator.userAgent.match(\/Trident.*rv:11\\.\/),i=d.querySelectorAll(\"iframe.wp-embedded-content\"),n=0;n<i.length;n++){if(!(r=(t=i[n]).getAttribute(\"data-secret\")))r=Math.random().toString(36).substr(2,10),t.src+=\"#?secret=\"+r,t.setAttribute(\"data-secret\",r);if(s||a)(e=t.cloneNode(!0)).removeAttribute(\"security\"),t.parentNode.replaceChild(e,t);t.contentWindow.postMessage({message:\"ready\",secret:r},\"*\")}}}}(window,document);\n<\/script>\n","description":"In this contributed article, technology writer and blogger Kayla Matthews discusses the field of data science is as unclear and vague as a muddy lake and it is critical to properly communicate results. While most terms and concepts include a legitimate definition, it\u2019s all too easy to get bogged down in technical jargon. As such, some ideas mean different things from company to company and even \u2014 in some cases \u2014 from project to project.","thumbnail_url":"https:\/\/insidebigdata.com\/wp-content\/uploads\/2016\/12\/Kayla-Matthews.png"}