Back when I was studying physics, I\u2019d often need to look up fancy-sounding technical terminology on Google. And when I did, more often than not, the first hit would be a Wikipedia article.<\/p>\n
But as great as Wikipedia entries are, it never quite seemed as though they were written at my level. They almost always went way over my head or seemed far too simple for my use case. This happened so consistently that I\u2019m tempted to call it a Law of the Internet: \u201cNo technical Wikipedia entry can be simultaneously comprehensible and informative\u201d.<\/p>\n
I think a lot of data science career advice (or job search\/interview preparation advice) follows a similar law: there are posts aimed at complete beginners, posts aimed at veteran software engineers, and posts designed to help junior data scientists hone their skills. All of this noise makes it difficult for many aspiring data scientists to know where to invest their time as they look to transition into the field.<\/p>\n
This is one of the main things I focus on when I work with mentees on\u00a0SharpestMinds<\/a>. And although there\u2019s no one-size-fits-all solution for everyone, I\u2019ve found that I consistently give the same advice to about 3 different categories of people.<\/p>\n If you\u2019re just breaking into data science, keep this in mind: the field is evolving very fast, so any advice that I give here will almost certainly be out of date by the time you\u2019re job-ready. What got people hired back in 2017 doesn\u2019t work today, and the disparity between data science hiring standards today and those that will apply one or two years from now will probably be even bigger.<\/p>\n With that out of the way, here are some pieces of advice if you\u2019re looking to break into data science today, and you don\u2019t already have a coding\/STEM background:<\/p>\n How to brand yourself:\u00a0<\/strong>if you get to the point where you\u2019re ready to apply for jobs, you might be surprised to learn that building a personal brand is unusually important in data science. And you might worry that because you don\u2019t have any professional experience, or a graduate degree in CS, branding might be a problem. But that can actually be your biggest brand advantage: you\u2019re the self-made, self-taught developer\/data scientist who companies can count on to learn fast and work hard\u2122. The catch is that the burden is on you to live up to that image: it\u2019s a steep hill to climb, but the reward can definitely be worth it.<\/p>\n Probably 20% of the aspiring data scientists I run into are software engineers. On one hand, having experience deploying code to production, and working with teams of developers can be a great asset. On the other, demand for fullstack developers is so high that companies sometimes end up nudging software engineers in that direction, even if the role they were hired for involved \u201cdata science\u201d on paper. So you\u2019ll want to avoid being pigeon-holed as a software engineer rather than a data scientist.<\/p>\n Some other thoughts:<\/p>\n How to brand yourself:<\/strong>\u00a0one of the easiest ways to brand yourself is by leveraging your experience in software development. You already know how to write clean, well-documented code, and how to collaborate with others, and that\u2019s a strength that isn\u2019t shared by most applicants to junior-level positions. But to effectively lean into your \u201cclean production code\u201d brand, you\u2019ll have to understand the analogous best practices in data science too, so be sure to tick that box if you can.<\/p>\n If you\u2019re a new undergraduate, Master\u2019s or PhD STEM grad, you probably have a good foundation in statistics and math. But you\u2019ve probably never applied for a job in tech, and you\u2019re not sure how to prepare for interviews. Also, assuming you\u2019ve been programming during your degree, you most likely can\u2019t write clean, well-organized code.<\/p>\n A few things to keep in mind:<\/p>\n How to brand yourself:<\/strong>\u00a0especially if you\u2019re a math or physics major, your best strategy is to cast yourself as someone with deep theoretical knowledge. To do that, you need to be able to confidently explain how various models work, and ideally to be familiar with the latest \u201chot\u201d results in the literature (especially true if you\u2019re aiming for a deep learning role).<\/p>\n ****<\/p>\n Caveat:\u00a0<\/strong>the advice I\u2019ve provided isn\u2019t going to map perfectly onto every situation. Some software engineers have further to go than others, and some total beginners have a knack for math and might be best suited to becoming deep learning researchers. But it should provide a good starting point for biasing the direction of your skills development.<\/p>\n At the end of the day, whether you\u2019re a software engineer, a recent grad, or a complete beginner, a key question to ask yourself is what career trajectories are closest to you in parameter space. If a stint as a data analyst, or a data visualization specialist is necessary to get your foot in the door, that can often be the best way to launch you on the right long-term trajectory.<\/p>\n","protected":false},"excerpt":{"rendered":" A lot of data science career advice follows a similar law: there are posts aimed at complete beginners, posts aimed at veteran software engineers, and posts designed to help junior data scientists hone their skills. All of this noise makes it difficult for many aspiring data scientists to know where to invest their time as they look to transition into the field. At the end of the day, whether you’re a software engineer, a recent grad, or a complete beginner, a key question to ask yourself is what career trajectories are closest to you in parameter space. <\/p>\n","protected":false},"author":251,"featured_media":3967,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"content-type":"","footnotes":""},"categories":[187],"tags":[94],"ppma_author":[2882],"class_list":["post-1538","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-bigdata-cloud","tag-data-science"],"authors":[{"term_id":2882,"user_id":251,"is_guest":0,"slug":"jeremie-harris","display_name":"Jeremie Harris","avatar_url":"https:\/\/secure.gravatar.com\/avatar\/?s=96&d=mm&r=g","user_url":"","last_name":"Harris","first_name":"Jeremie","job_title":"","description":"Jeremie Harris is Co-Founder at SharpestMinds<\/a> that finds new grads their first jobs in machine learning and data science. He has many publications to his credit."}],"_links":{"self":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/1538","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\/251"}],"replies":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/comments?post=1538"}],"version-history":[{"count":4,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/1538\/revisions"}],"predecessor-version":[{"id":30097,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/posts\/1538\/revisions\/30097"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media\/3967"}],"wp:attachment":[{"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/media?parent=1538"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/categories?post=1538"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/tags?post=1538"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/www.experfy.com\/blog\/wp-json\/wp\/v2\/ppma_author?post=1538"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Category 1: complete beginners<\/h3>\n
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Category 2: software engineers<\/h3>\n
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Category 3: new CS, math or physics\u00a0grads<\/h3>\n
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