{"id":859,"date":"2018-08-29T02:31:27","date_gmt":"2018-08-29T02:31:27","guid":{"rendered":"http:\/\/kusuaks7\/?p=464"},"modified":"2023-07-26T14:55:18","modified_gmt":"2023-07-26T14:55:18","slug":"what-on-earth-is-data-science","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/software-ux-ui\/what-on-earth-is-data-science\/","title":{"rendered":"What on earth is data science?"},"content":{"rendered":"<p><strong><em>Ready to learn Data Science? Browse\u00a0<a href=\"https:\/\/www.experfy.com\/training\/tracks\/data-science-training-certification\">Data Science Training and Certification<\/a> courses developed by industry thought leaders and Experfy in Harvard Innovation Lab.<\/em><\/strong><\/p>\n<h3 id=\"9c78\"><strong>The quest for a useful definition<\/strong><\/h3>\n<figure id=\"a100\" data-scroll=\"native\"><canvas width=\"75\" height=\"32\"><\/canvas><img decoding=\"async\" style=\"width: 700px; height: 309px;\" src=\"https:\/\/cdn-images-1.medium.com\/max\/2000\/1*BYa4iRkCdeY5lUCHMpcq6g.png\" data-src=\"https:\/\/cdn-images-1.medium.com\/max\/2000\/1*BYa4iRkCdeY5lUCHMpcq6g.png\" \/><\/figure>\n<p id=\"92ae\">Behold my pithiest attempt: \u201c<strong>Data science is the discipline of making data useful<\/strong>.\u201d Feel free to flee now or stick around of a tour of its three subfields.<\/p>\n<ul>\n<li id=\"66fa\"><a href=\"https:\/\/towardsdatascience.com\/statistics-for-people-in-a-hurry-a9613c0ed0b\" target=\"_blank\" rel=\"noopener\" data-href=\"https:\/\/towardsdatascience.com\/statistics-for-people-in-a-hurry-a9613c0ed0b\" data->Statistics<\/a><\/li>\n<li id=\"1382\"><a href=\"https:\/\/hackernoon.com\/the-simplest-explanation-of-machine-learning-youll-ever-read-bebc0700047c\" target=\"_blank\" rel=\"noopener\" data-href=\"https:\/\/hackernoon.com\/the-simplest-explanation-of-machine-learning-youll-ever-read-bebc0700047c\" data->Machine learning<\/a><\/li>\n<li id=\"2299\"><a href=\"https:\/\/hackernoon.com\/top-10-roles-for-your-data-science-team-e7f05d90d961\" target=\"_blank\" rel=\"noopener\" data-href=\"https:\/\/hackernoon.com\/top-10-roles-for-your-data-science-team-e7f05d90d961\" data->Data-mining \/ analytics<\/a><\/li>\n<\/ul>\n<h4 id=\"c305\"><strong>The term no one really\u00a0defined<\/strong><\/h4>\n<p id=\"4e2a\">If you poke around in the early\u00a0<a href=\"https:\/\/www.forbes.com\/sites\/gilpress\/2013\/05\/28\/a-very-short-history-of-data-science\/#6b64132f55cf\" target=\"_blank\" rel=\"noopener\" data-href=\"https:\/\/www.forbes.com\/sites\/gilpress\/2013\/05\/28\/a-very-short-history-of-data-science\/#6b64132f55cf\" data->history<\/a>\u00a0of the term\u00a0<strong>data science<\/strong>, you see two themes coming together. Allow me to paraphrase for your amusement:<\/p>\n<ul>\n<li id=\"6e9a\">Big(ger) data means more tinkering with computers.<\/li>\n<li id=\"d396\"><a href=\"https:\/\/towardsdatascience.com\/statistics-for-people-in-a-hurry-a9613c0ed0b\" target=\"_blank\" rel=\"noopener\" data-href=\"https:\/\/towardsdatascience.com\/statistics-for-people-in-a-hurry-a9613c0ed0b\" data->Statisticians<\/a>\u00a0can\u2019t code their way out of a paper bag.<\/li>\n<\/ul>\n<p id=\"71de\">And thus, data science is born. The way I first heard the job defined is \u201cA\u00a0<strong>data scientist\u00a0<\/strong>is a\u00a0<strong>statistician<\/strong>\u00a0who can code.\u201d I\u2019ll be full of opinions on that\u00a0<a href=\"https:\/\/medium.com\/@kozyrkov\/is-data-science-a-bubble-c70ceac0f264\" target=\"_blank\" rel=\"noopener\" data-href=\"https:\/\/medium.com\/@kozyrkov\/is-data-science-a-bubble-c70ceac0f264\" data->in a moment<\/a>, but first, why don\u2019t we examine data science itself?<\/p>\n<figure id=\"12ec\"><canvas width=\"75\" height=\"50\"><\/canvas><img decoding=\"async\" style=\"width: 608px; height: 409px;\" src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/1*NcQRRnvZdcqbnN0lm99k2w.png\" data-src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/1*NcQRRnvZdcqbnN0lm99k2w.png\" \/><\/figure>\n<p style=\"text-align: center;\"><a href=\"https:\/\/twitter.com\/cdixon\/status\/428914681911070720\" target=\"_blank\" rel=\"nofollow noopener\" data-href=\"https:\/\/twitter.com\/cdixon\/status\/428914681911070720\" data->Twitter definitions<\/a>\u00a0circa\u00a02014.<\/p>\n<p id=\"adce\">I love how the 2003 launch of\u00a0<a href=\"http:\/\/www.jds-online.com\/\" target=\"_blank\" rel=\"noopener\" data-href=\"http:\/\/www.jds-online.com\/\" data-><em>Journal of Data Science<\/em><\/a>\u00a0goes right for the narrowest possible scope:\u00a0<em>\u201cBy \u2018Data Science\u2019 we mean almost\u00a0<\/em><strong><em>everything<\/em><\/strong><em>\u00a0that has something to do with data.\u201d\u00a0<\/em>So\u2026 everything, then? It\u2019s hard to think of something that has nothing to do with information. (I should stop thinking about this before my head explodes.)<\/p>\n<p id=\"c983\">Since then, we\u2019ve seen a multitude of opinions, from Conway\u2019s well-traveled Venn diagram (<em>below<\/em>) to Mason and Wiggins\u2019\u00a0<a href=\"http:\/\/www.dataists.com\/2010\/09\/a-taxonomy-of-data-science\/\" target=\"_blank\" rel=\"noopener\" data-href=\"http:\/\/www.dataists.com\/2010\/09\/a-taxonomy-of-data-science\/\" data->classic post<\/a>.<\/p>\n<figure id=\"820f\"><canvas width=\"75\" height=\"70\"><\/canvas><img decoding=\"async\" style=\"width: 700px; height: 667px;\" src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/0*Vv3ODEuDmp7bDkyu.jpg\" data-src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/0*Vv3ODEuDmp7bDkyu.jpg\" \/><figcaption>\u00a0<\/figcaption><\/figure>\n<p id=\"c5a2\" style=\"text-align: center;\"><a href=\"http:\/\/drewconway.com\/\" target=\"_blank\" rel=\"noopener\" data-href=\"http:\/\/drewconway.com\/\" data->Drew Conway<\/a>\u2019s definition of data science. My personal taste runs more towards the definition on\u00a0<a href=\"https:\/\/en.wikipedia.org\/wiki\/Data_science\" target=\"_blank\" rel=\"noopener\" data-href=\"https:\/\/en.wikipedia.org\/wiki\/Data_science\" data->Wikipedia<\/a>.<\/p>\n<p><strong>Wikipedia<\/strong>\u00a0has one that\u2019s very close to what I teach my students:<\/p>\n<p id=\"6617\"><em>Data science is a \u2018concept to unify statistics, data analysis, machine learning and their related methods\u2019 in order to \u2018understand and analyze actual phenomena\u2019 with data.<\/em><\/p>\n<p id=\"b170\">That\u2019s a mouthful, so let me see if I can make it short and sweet:<\/p>\n<blockquote id=\"9e23\"><p>\u201cData science is the discipline of making data\u00a0useful.\u201d<\/p><\/blockquote>\n<p id=\"383f\">What you\u2019re thinking around about now might be,\u00a0<em>\u201cNice try, Cassie. It\u2019s cute, but it\u2019s an egregiously lossy reduction. How does the word \u2018useful\u2019 capture all of that jargon stuff?\u201d<\/em><\/p>\n<p id=\"352b\">Well, okay, lets argue it out with pictures.<\/p>\n<figure id=\"ee84\"><canvas width=\"75\" height=\"32\"><\/canvas><img decoding=\"async\" src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/1*1agVRJ_7_HwrKW81jQamwQ.png\" data-src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/1*1agVRJ_7_HwrKW81jQamwQ.png\" \/><\/figure>\n<p id=\"8555\" style=\"text-align: center;\">Here\u2019s a map for data science for you, perfectly faithful to the\u00a0<a href=\"https:\/\/en.wikipedia.org\/wiki\/Data_science\" target=\"_blank\" rel=\"noopener\" data-href=\"https:\/\/en.wikipedia.org\/wiki\/Data_science\" data->Wikipedia<\/a>\u00a0definition.<\/p>\n<p>What are these things and how do you know where you are on the map?<\/p>\n<p id=\"fc25\">If you\u2019re about try breaking them down by\u00a0<strong>standard toolkits<\/strong>, slow down. The difference between a statistician and a machine learning engineer is not that one uses R and the other uses Python. The SQL vs R vs Python taxonomy is ill-advised for so many reasons, not least of which is that software evolves. (As of recently, you can even do\u00a0<a href=\"https:\/\/cloud.google.com\/bigquery\/docs\/bigqueryml-intro\" target=\"_blank\" rel=\"noopener\" data-href=\"https:\/\/cloud.google.com\/bigquery\/docs\/bigqueryml-intro\" data->ML in SQL<\/a>.) Wouldn\u2019t you prefer a breakdown that\u2019ll last? In fact, just go ahead and unread this entire paragraph.<\/p>\n<p id=\"edf8\">Perhaps worse is the favorite way novices split the space. Yup, you guessed it: by the\u00a0<strong>algorithm<\/strong>\u00a0(surprise! it\u2019s how university courses are structured). Pretty please, don\u2019t taxonomize by histograms vs t-tests vs neural networks. Frankly, if you\u2019re clever and you have a point to make, you can use the same algorithm for any part of data science. It might look like Frankenstein\u2019s monster, but I assure you it can be forced to do your bidding.<\/p>\n<p id=\"a550\">Enough with the dramatic buildup! Here\u2019s the taxonomy I propose:<\/p>\n<figure id=\"b787\"><canvas width=\"75\" height=\"27\"><\/canvas><img decoding=\"async\" style=\"width: 700px; height: 266px;\" src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/1*TilIsSqBjH-QlYs4valU4Q.png\" data-src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/1*TilIsSqBjH-QlYs4valU4Q.png\" \/><\/figure>\n<h4 id=\"91a6\"><strong>None-One-Many<\/strong><\/h4>\n<p id=\"0012\">What on earth is this? Why, decisions, of course! (Under\u00a0<em>incomplete information<\/em>. When all the facts you need are visible to you, you can use descriptive analytics for making as many decisions as you please. Just look at the facts and you\u2019re done.)<\/p>\n<blockquote id=\"0741\"><p>It\u2019s through our actions\u200a\u2014\u200aour decisions\u200a\u2014\u200athat we affect the world around\u00a0us.<\/p><\/blockquote>\n<p id=\"485c\">I\u2019d promised you we were going to talk about making data useful. To me, the idea of usefulness is tightly coupled with influencing real-world actions. If I believe in Santa Claus, it doesn\u2019t particularly matter unless it might influence my behavior in some way. Then, depending on the potential consequences of that behavior, it might start to matter an awful lot. It\u2019s through our actions \u2014 our decisions \u2014 that we affect the world around us (and invite it to affect us right back).<\/p>\n<p id=\"0113\">So here\u2019s the new decision-oriented picture for you, complete with the three main ways to make your data useful.<\/p>\n<figure id=\"230d\"><canvas width=\"75\" height=\"25\"><\/canvas><img decoding=\"async\" style=\"width: 700px; height: 235px;\" src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/1*8Wz6lQ8GFEAvnSS5uqMQ5g.png\" data-src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/1*8Wz6lQ8GFEAvnSS5uqMQ5g.png\" \/><\/figure>\n<h4 id=\"edb3\"><strong>Data-mining<\/strong><\/h4>\n<p id=\"5cf1\">If you don\u2019t know what decisions you want to make yet, the best you can do is go out there in search of inspiration. That\u2019s called data-mining or analytics or descriptive analytics or exploratory data analysis (EDA) or knowledge discovery (KD), depending on which crowd you hung out with during your impressionable years.<\/p>\n<blockquote id=\"617a\"><p>Golden rule of analytics: only make conclusions about what you can\u00a0see.<\/p><\/blockquote>\n<p id=\"575e\">Unless you know how you intend to frame your decision-making, start here. The great news is that this one is easy. Think of your dataset as a bunch of negatives you found in a darkroom. Data-mining is about working the equipment to expose all the images as quickly as possible so you can see whether there\u2019s anything inspiring on them. As with photos, remember not to take what you see too seriously. You didn\u2019t take the photos, so you don\u2019t know much about what\u2019s off-screen. The golden rule of data-mining is:\u00a0<strong>stick to what is here.<\/strong>\u00a0Only make conclusions about what you can see, never about what you can\u2019t see (for that you need statistics and lot more expertise).<\/p>\n<p id=\"f6ed\">Other than that, you can do no wrong. Speed wins, so start practicing.<\/p>\n<blockquote id=\"e799\"><p>Expertise in data-mining is judged by the speed with which you can examine your data. It helps not to snooze past the interesting nuggets.<\/p><\/blockquote>\n<p id=\"e701\">The darkroom\u2019s intimidating at first, but there\u2019s not that much to it. Just learn to work the equipment. Here\u2019s a tutorial in\u00a0<a href=\"https:\/\/www.datacamp.com\/community\/tutorials\/tidyverse-tutorial-r\" target=\"_blank\" rel=\"noopener\" data-href=\"https:\/\/www.datacamp.com\/community\/tutorials\/tidyverse-tutorial-r\" data->R<\/a>\u00a0and here\u2019s one in\u00a0<a href=\"https:\/\/www.datacamp.com\/community\/tutorials\/kaggle-machine-learning-eda\" target=\"_blank\" rel=\"noopener\" data-href=\"https:\/\/www.datacamp.com\/community\/tutorials\/kaggle-machine-learning-eda\" data->Python<\/a>\u00a0to get you started. You can call yourself a data analyst as soon as you start having fun and you can call yourself an expert analyst when you\u2019re able to expose photos (and all the other kinds of datasets) with lightning speed.<\/p>\n<h4 id=\"1ebb\"><strong>Statistical inference<\/strong><\/h4>\n<p id=\"7b35\">Inspiration is cheap, but rigor is expensive. If you want to leap beyond the data, you\u2019re going to need specialist training. As someone with undergrad\u00a0<em>and<\/em>graduate majors in statistics, I may be just a tad biased here, but in my opinion statistical inference (statistics for short) is the most difficult and philosophy-laden of the three areas. Getting good at it takes the most time.<\/p>\n<blockquote id=\"82f6\"><p>Inspiration is cheap, but rigor is expensive.<\/p><\/blockquote>\n<p id=\"1e8a\">If you intend to make high-quality, risk-controlled, important decisions that rely on conclusions about the world beyond the data available to you, you\u2019re going to have to bring statistical skills onto your team. A great example is that moment when your finger is hovering over the launch button for an AI system and it occurs to you that you need to check it works before releasing it (always a good idea, seriously). Step away from the button and call in the statistician.<\/p>\n<blockquote id=\"1f64\"><p>Statistics is the science of changing your mind (under uncertainty).<\/p><\/blockquote>\n<p id=\"ee3a\">If you want to learn more, I\u2019ve written\u00a0<a href=\"https:\/\/towardsdatascience.com\/statistics-for-people-in-a-hurry-a9613c0ed0b\" target=\"_blank\" rel=\"noopener\" data-href=\"https:\/\/towardsdatascience.com\/statistics-for-people-in-a-hurry-a9613c0ed0b\" data->this 8-minute super-summary of statistics<\/a>\u00a0for your enjoyment.<\/p>\n<h4 id=\"4e0a\"><strong>Machine learning<\/strong><\/h4>\n<p id=\"64d4\">Machine learning is essentially\u00a0<a href=\"https:\/\/hackernoon.com\/the-simplest-explanation-of-machine-learning-youll-ever-read-bebc0700047c\" target=\"_blank\" rel=\"noopener\" data-href=\"https:\/\/hackernoon.com\/the-simplest-explanation-of-machine-learning-youll-ever-read-bebc0700047c\" data->making thing-labeling recipes using examples instead of instructions.<\/a>\u00a0I\u2019ve written a few posts about it, including whether it\u2019s\u00a0<a href=\"https:\/\/medium.com\/@kozyrkov\/are-you-using-the-term-ai-incorrectly-911ac23ab4f5\" target=\"_blank\" rel=\"noopener\" data-href=\"https:\/\/medium.com\/@kozyrkov\/are-you-using-the-term-ai-incorrectly-911ac23ab4f5\" data->different from AI<\/a>, how to\u00a0<a href=\"https:\/\/hackernoon.com\/imagine-a-drunk-island-advice-for-finding-ai-use-cases-8d47495d4c3f\" target=\"_blank\" rel=\"noopener\" data-href=\"https:\/\/hackernoon.com\/imagine-a-drunk-island-advice-for-finding-ai-use-cases-8d47495d4c3f\" data->get started<\/a>\u00a0with it,\u00a0<a href=\"https:\/\/hackernoon.com\/why-businesses-fail-at-machine-learning-fbff41c4d5db\" target=\"_blank\" rel=\"noopener\" data-href=\"https:\/\/hackernoon.com\/why-businesses-fail-at-machine-learning-fbff41c4d5db\" data->why businesses fail at it<\/a>, and the first couple of articles in a series of plain-language takes on the jargon nitty gritties (<a href=\"https:\/\/towardsdatascience.com\/explaining-supervised-learning-to-a-kid-c2236f423e0f\" target=\"_blank\" rel=\"noopener\" data-href=\"https:\/\/towardsdatascience.com\/explaining-supervised-learning-to-a-kid-c2236f423e0f\" data->start here<\/a>). Oh, and if you want to share them with non-English-speaking friends, a bunch of them are translated\u00a0<a href=\"https:\/\/medium.com\/@kozyr_91350\/\" target=\"_blank\" rel=\"noopener\" data-href=\"https:\/\/medium.com\/@kozyr_91350\/\" data->here<\/a>.<\/p>\n<figure id=\"fb8d\"><canvas width=\"75\" height=\"52\"><\/canvas><img decoding=\"async\" style=\"width: 700px; height: 494px;\" src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/0*hhHRjIyPDYa3fois.jpg\" data-src=\"https:\/\/cdn-images-1.medium.com\/max\/800\/0*hhHRjIyPDYa3fois.jpg\" \/><\/figure>\n<h4 id=\"503f\"><strong>Data engineering<\/strong><\/h4>\n<p id=\"d84f\">What about\u00a0<a href=\"https:\/\/www.dataquest.io\/blog\/what-is-a-data-engineer\/\" target=\"_blank\" rel=\"noopener\" data-href=\"https:\/\/www.dataquest.io\/blog\/what-is-a-data-engineer\/\" data-><strong>data engineering<\/strong><\/a>, the work that delivers data to the data science team in the first place? Since it\u2019s a sophisticated field in its own right, I prefer to shield it from data science\u2019s hegemonic aspirations. Besides, it\u2019s much closer in species to software engineering than to statistics.<\/p>\n<blockquote id=\"cfa9\"><p>The difference between data engineering and data science is a difference of before and\u00a0after.<\/p><\/blockquote>\n<p id=\"77c4\">Feel free to see the\u00a0<strong>data engineering versus data science<\/strong>\u00a0difference as\u00a0<strong>before versus after<\/strong>. Most of the technical work leading up to the birthing of the data (before) may comfortably be called \u201cdata engineering\u201d and everything we do once some data have arrived (after) is \u201cdata science\u201d.<\/p>\n<h4 id=\"b76b\"><strong>Decision intelligence<\/strong><\/h4>\n<p id=\"89df\"><a href=\"https:\/\/www.youtube.com\/watch?v=x1k37Na1iLc\" target=\"_blank\" rel=\"noopener\" data-href=\"https:\/\/www.youtube.com\/watch?v=x1k37Na1iLc\" data->DI is all about decisions<\/a>, including decision-making at scale with data, which makes it an engineering discipline. It augments the applied aspects of data science with ideas from the social and managerial sciences.<\/p>\n<blockquote id=\"4e04\"><p>Decision intelligence adds components from the social and managerial sciences.<\/p><\/blockquote>\n<p id=\"cb05\">In other words, it\u2019s a superset of those bits of data science not concerned with researchy things like creating fundamental methodologies for general-purpose use.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Data science is the discipline of making data useful. Data science is a &lsquo;concept to unify statistics, data analysis, machine learning and their related methods&rsquo; in order to &lsquo;understand and analyze actual phenomena&rsquo; with data. When all the facts you need are visible to you, you can use descriptive analytics for making as many decisions as you please. It&rsquo;s through our actions\u200a&mdash;\u200aour decisions\u200a&mdash;\u200athat we affect the world around&nbsp;us. So it is making data useful.<\/p>\n","protected":false},"author":335,"featured_media":2741,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[200],"tags":[],"ppma_author":[2050],"class_list":["post-859","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-software-ux-ui"],"authors":[{"term_id":2050,"user_id":335,"is_guest":0,"slug":"cassie-kozyrkov","display_name":"Cassie Kozyrkov","avatar_url":"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2020\/04\/medium_df35f80d-2bff-4fe3-b741-a94d51320e00-150x150.jpg","user_url":"https:\/\/careers.google.com\/?src=Online\/LinkedIn\/linkedin_profilepage&amp;utm_source","last_name":"Kozyrkov","first_name":"Cassie","job_title":"","description":"Cassie Kozyrkov is Chief Decision Scientist at Google, Inc. With a unique combination of deep technical expertise, and world-class public-speaking skills, she has provided guidance on more than 100 projects and designed Google's analytics program, personally training over 15000 Googlers in statistics, decision-making, and machine learning.\u00a0"}],"_links":{"self":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/859","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/users\/335"}],"replies":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/comments?post=859"}],"version-history":[{"count":2,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/859\/revisions"}],"predecessor-version":[{"id":29605,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/859\/revisions\/29605"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/2741"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=859"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=859"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=859"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=859"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}