{"id":10196,"date":"2020-10-12T10:30:08","date_gmt":"2020-10-12T10:30:08","guid":{"rendered":"https:\/\/www.experfy.com\/blog\/?p=10196"},"modified":"2023-10-24T13:30:36","modified_gmt":"2023-10-24T13:30:36","slug":"why-you-shouldnt-perfectionist-data-scientist","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/bigdata-cloud\/why-you-shouldnt-perfectionist-data-scientist\/","title":{"rendered":"Why you shouldn\u2019t be a Perfectionist as a Data Scientist"},"content":{"rendered":"\t\t
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\n\t\t\t\t\t\t\t\t\tAnd how to overcome the perfectionist in you<\/em>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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I\u2019m not sure about you.<\/i><\/p>\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<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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\n\t\t\t\t\t\t\t\t\tOr maybe you\u2019ve struck a balance between being realistic and chasing perfection at the same time.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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But chasing perfection has always been something that I\u2019m guilty of.<\/p>\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<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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\n\t\t\t\t\t\t\t\t\tI\u2019m not saying that chasing perfection is a bad thing, but chasing perfection\u00a0\u201ccould\u201d\u00a0<\/em><\/strong>be a bad thing, especially when you\u2019re in data science field.\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<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\u201cPerfection is not attainable, but if we chase perfection we can catch excellence\u201d<\/p>\n

\u2014 Vince Lombardi<\/p>\n<\/blockquote>\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<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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\n\t\t\t\t\t\t\t\t\tAfter being quite a while in data science field upon my transition from academic to data science path, I\u2019ve learned so much when it comes to being able to deliver business value while still having the bandwidth to make an excellent deliverable.\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<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tHence I hope to share my thoughts and experience with you to hopefully give you the awareness in your day-to-day data science work and how to overcome the perfectionist residing in you.\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<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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By the end of this article, I hope\u00a0you\u2019ll understand better why chasing perfectionism might not be a good idea as a data scientist and how to overcome that in your career \u2014 or perhaps, in life.<\/strong><\/p>\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<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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Let\u2019s get started!<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Why you shouldn\u2019t be a Perfectionist as a Data Scientist<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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You see.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

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\n\t\t\t\t\t\t\t\t\tWhen you\u2019re a beginner in data science field, it\u2019s easy to fall into the trap of chasing perfection\u00a0blindly<\/strong>\u00a0without understanding <\/a>the real needs of the business in your company.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tImagine you\u2019ve just joined a company as a junior data scientist, and now you\u2019re given a project to build a deep learning model for image classification and put it into production in six months.\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<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tNeedless to say, you\u2019re so excited about the project and already bouncing some ideas around with your team members.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tSix months after, you miss the deadline for final deliverable as you still think that your model is not good enough, despite the fact that your model has already outperformed the existing baseline model.\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<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tSo you keep tuning your model to hopefully squeeze the last bit of improved accuracy out by 2%\u2026\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tAnd because of this, you\u00a0miss the deadline<\/strong>\u00a0for the project deliverable (putting the model into production) and\u00a0your boss is skeptical of your credential and capability as a data scientist<\/strong>. What happens next? Now\u00a0you become frustrated<\/strong>\u00a0and wonder why your boss simply doesn\u2019t understand your work.\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<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Does that sound familiar to you, somehow?<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\u201cDone is better than perfect\u201d<\/mark><\/p>\n<\/blockquote>\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<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t

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\n\t\t\t\t\t\t\t\t\tIn fact, the similar case happened to me when I first got started in a data science internship last time.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tI read a lot of research papers.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tI tried to train and tune different kind of models to find the best one without having a full understanding of the business needs.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tWhat I knew was that I got to get the best model ever like what I did in any of the Kaggle competitions.This is exactly the difference between Kaggle competitions and real life business projects.<\/strong>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tIn\u00a0Kaggle<\/strong>, you\u2019re your only stakeholder and you just have to train the best model and \u201cboom!\u201d\u2026 You win the competition.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tIn\u00a0real life business projects<\/strong>, this is not the case. You have your bosses and other stakeholders with limited resources (time or $$) to deliver the final results.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tAnd now, what I\u2019ve realized is that delivering real business value is the ultimate objective in any data science projects. And if you\u2019re a perfectionist, chances are you can\u2019t always deliver solutions to your customers because you might fail to deliver\u00a0Minimum Viable Product (MVP)<\/strong><\/a>to them.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tWith MVP and feedback from your customers, you can then further iterate and improve the future product development (or data science solutions, whatever name that you may call it).\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tIf you\u2019re a perfectionist<\/strong>, you\u2019ll always tend to set unrealistic expectation of yourself and everyone.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tIf you\u2019re a perfectionist<\/strong>, you\u2019ll find yourself struggling to get things done (or you may think your work is always not good enough to be considered done)\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tIf you\u2019re a perfectionist<\/strong>, you\u2019ll be afraid of making mistakes and that deprives you of any opportunity to learn and improve.\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<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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How to overcome the perfectionist in you<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tKnowing when NOT to be a perfectionist\u00a0<\/a>could sometimes be a blessing in disguise.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tTo be honest with you, I admit that the perfectionist in me still exists. And I think this is still a good thing, if and only if I know how to go for perfection \u201ctimely\u201d.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tThat being said, here are some of the steps that I personally use whenever I realize that I tend to procrastinate (I mean to go for perfection).<\/p>\n\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<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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1. Be aware of it<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tYes. Self-awareness is key here.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tIf you\u2019re not even aware of the moment when you start going for perfection, there is no way that you can overcome that. In other words,\u00a0to overcome the perfectionist in you, first you need to be aware of that<\/strong>.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tStart questioning yourself why you\u2019re doing what you\u2019re doing and what you\u2019re doing will truly benefit the business value. By having this inner conversation with yourself, you\u2019ll begin to prioritize the right things at the right time.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tI know this sounds a bit abstract. So take your time to digest and practise.\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<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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2. Challenge yourself to establish more realistic expectations of yourself<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tCreate a Minimum Viable Product (MVP) and understand\u00a0\u201cwhat\u2019s good enough\u201d\u00a0<\/strong>and deliver that to your customers.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tDepending on what problems and industry that you\u2019re solving, there are certain areas where less-than-perfect is perfectly acceptable.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tBe prepared to set those realistic expectations of yourself instead of going down the infinite rabbit hole in the name of deeper investigation and making results better. Nevertheless, the ideal case is always to go deeper and make results better (or more accurate), provided that time and other resources allow.\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<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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3. Accept mistakes and failure<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tThere is no absolute perfection. Because there is always room for improvement.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tEmbrace failure and the fact that you\u2019re going to make mistakes along the way.\u00a0Just be resilient and learn from your mistakes to improve.<\/strong>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tThis way you\u2019ll be able to overcome the perfectionist in you by constantly making progress towards your goals and improve along the way.\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<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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Final Thoughts<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tThank you for reading.\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<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t
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\n\t\t\t\t\t\t\t\t\tI hope by now you already understand better why chasing perfectionism might not be a good idea as a data scientist and how to overcome that in your career.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\tWhile chasing perfection is perfectly fine, it\u2019s up to you to decide and prioritize what\u2019s the most important task to do, depending on your situation and context.\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<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"

While chasing perfection is perfectly fine, it\u2019s up to you to decide and prioritize what\u2019s the most important task to do, depending on your situation and context.<\/p>\n","protected":false},"author":493,"featured_media":10197,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[187],"tags":[94,394,718],"ppma_author":[1922],"class_list":["post-10196","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-bigdata-cloud","tag-data-science","tag-data-scientist","tag-perfectionist"],"authors":[{"term_id":1922,"user_id":493,"is_guest":0,"slug":"admond-lee-kin-lim","display_name":"Admond Lee","avatar_url":"https:\/\/www.experfy.com\/blog\/wp-content\/uploads\/2020\/04\/medium_0878f2cd-4527-4f07-8f34-5711c7f91a5b-150x150.jpg","user_url":"http:\/\/www.micron.com","last_name":"Lee","first_name":"Admond","job_title":"","description":"Admond Lee Kin Lim\u00a0is Big Data Engineer at Micron Technology<\/a>, a world leader in innovative memory solutions that transform how the world uses information."}],"_links":{"self":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/10196","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\/493"}],"replies":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/comments?post=10196"}],"version-history":[{"count":5,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/10196\/revisions"}],"predecessor-version":[{"id":33682,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/10196\/revisions\/33682"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/10197"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=10196"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=10196"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=10196"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=10196"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}