{"id":17348,"date":"2017-03-09T05:00:56","date_gmt":"2017-03-09T13:00:56","guid":{"rendered":"http:\/\/insidebigdata.com\/?p=17348"},"modified":"2017-03-10T08:38:50","modified_gmt":"2017-03-10T16:38:50","slug":"making-leap-data-science-hopeful-practitioner","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2017\/03\/09\/making-leap-data-science-hopeful-practitioner\/","title":{"rendered":"Making the Leap from Data Science Hopeful to Practitioner"},"content":{"rendered":"

It\u2019s a familiar dilemma. You\u2019ve done your research, read some books, taken some online classes \u2013 and at long last, you\u2019re finally ready to get real-life work experience as a Data Scientist.<\/p>\n

But as you browse job postings, you become discouraged: \u201cThey want me to be a d3 Expert and <\/i>a Deep Learning \u2018Ninja\u2019? A \u2018Wizard\u2019 of ETL and <\/i>a tidyverse-loving #Rstats \u2018Samurai\u2019? What does it even mean to be a Viking of scikit-learn by ascending to the Valhalla of XGBoost? Is that, like, two years of work experience? Three?\u201d<\/p>\n

As you add a few more classes to your ever-expanding study plan, you let out a sigh: \u201cMaybe Data Science can be my second career when I retire.\u201d<\/p>\n

While I believe that lifelong learning is the universe\u2019s most powerful force for good, it\u2019s easy to overdo. If you\u2019ve developed a solid foundation, you may be ready to look for your first job \u2013 even if your resume doesn\u2019t perfectly match the requirements for your dream position. This is particularly true in Data Science, where no single human being will ever be qualified for your average entry-level job posting.<\/p>\n

And while we\u2019re not going to fix the Data Science hiring market in a single article, I can at minimum offer my humble perspective on how to get genuine Data Science experience on your resume.<\/p>\n

1. Build a portfolio<\/b><\/p>\n

This advice almost certainly counts as cliched; however, like many great cliches, it contains some profound truth if you can keep yourself from rolling your eyes.<\/p>\n

Let\u2019s put ourselves in the shoes of a hypothetical Data Science hiring manager. We\u2019re juggling a handful of brilliant-but-unruly direct reports and a dozen or so crunch-time projects. Last week, the boss walked in with great news \u2013 we\u2019re going to lead a big, important new initiative! And sure, we can hire someone to help. So now we\u2019re staring at your job application, trying to figure out if you\u2019re interested and competent – and we need to do it quickly.<\/p>\n

Ah, but you\u2019ve included a portfolio. We open it and see a few well-thought-out projects that demonstrate a range of skills. Anyone who took time to put this together is interested. Anyone who could<\/i> put this together is probably competent. We\u2019re intrigued by a particular project. We have a few questions. Could you come in tomorrow for an interview?<\/i><\/p>\n

The above is a dramatization, of course, but I will always \u2013 100 times out of 100 \u2013 browse through somebody\u2019s Github or blog if they link to it. Moreover, you\u2019d be surprised by how few people do this; it really does differentiate you, and can actually be almost as impactful a signal as previous Data Science experience. \u00a0As discussed above, \u201cData Science\u201d encompasses an enormous range of skills, so many hiring managers are looking more for evidence that you can learn new skills and apply them to real-life problems than they are for a laundry list of certifications.<\/p>\n

A portfolio isn\u2019t just a tool for hiring managers, either. Building a portfolio has profound personal benefits, including confidence (being able to speak to real things you\u2019ve done is good); genuine skill development (reading about things is never the same as actually doing things); and what Bill Burnett and Dave Evans call \u201cprototyping your life<\/a>,\u201d which in this case means figuring out if Data Science is something you really<\/i> want to do by experimenting with it in a lightweight, low-cost way.<\/p>\n

2. Build upon your present circumstances<\/b><\/p>\n

Though it\u2019s existed in various forms for some time, what we call \u201cData Science\u201d is still a new field. Nearly everyone who calls themselves Data Scientists got to where they are by making a transition from a more traditional career. For some – e.g., a statistician or a PhD in a technical field \u2013 the transition may have been straightforward.<\/p>\n

For others, however, it will have been more involved. I, for example, worked in finance: a data-rich field, but certainly not one that had embraced all of what Data Science had to offer. However, I saw this gap as an opportunity to improve both my field and my skills. By night, I took online classes (like Andrew Ng\u2019s Machine Learning class on Coursera), and by day, I applied what I learned to bring better tools or fresh perspective to my more quantitative projects. This enabled me to develop real-life expertise.<\/p>\n

So ask yourself: are there aspects of your current job that would benefit from a bit of Data Science magic? And if not, could you volunteer your burgeoning data skills for a non-profit, political campaign, or open source project? If so, start building experience now on a project-by-project basis. Even if your official job title doesn\u2019t change, those projects are great resume builders, help beef up a portfolio, and are great stories to discuss in interviews to demonstrate you\u2019re a true Data Science practitioner<\/p>\n

3. Consider a Master\u2019s program or bootcamp<\/b><\/p>\n

Portfolios and projects are wonderful, but sometimes you need immersive experience to take you the last mile. In my case, I went to a Data Science bootcamp, which allowed me to accelerate my skill development considerably; the bootcamp experience also gave me a cogent narrative for my transition from finance into Data Science. (Never underestimate the power of being able to construct a narrative for how you got from Point A to Point B.)<\/p>\n

The market for Data Science bootcamp-level curriculums has only grown stronger and more competitive in the past few years. In addition to a variety of in-person immersive options, there are also robust online programs \u2013 ranging from certificates built on series of courses to full online Data Science Master\u2019s programs. While this option isn\u2019t for everyone, I\u2019m glad I pursued it: after all, if you\u2019ve done enough prototyping, the next step is to go out and build something.<\/p>\n

\"\"Contributed by: Dan Saber, Data Science Hiring Manager at Coursera<\/a>, where he leads a team that develops the insights and algorithms powering the global online learning platform. Dan was a Fellow at Zipfian Academy, a 12-week intensive Data Science program where he honed his skills. Prior to his Data Science career, Dan worked in Investment Management as an Associate at Franklin Templeton Investments. He attended UCLA, where he earned a degree in Mathematics and Economics.<\/em><\/p>\n

 <\/p>\n

Sign up for the free insideBIGDATA\u00a0newsletter<\/a>.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

It\u2019s a familiar dilemma. You\u2019ve done your research, read some books, taken some online classes \u2013 and at long last, you\u2019re finally ready to get real-life work experience as a Data Scientist. In this contributed article, Dan Saber, Data Science Hiring Manager at Coursera, offers three important steps for successfully transitioning into a data science career. <\/p>\n","protected":false},"author":10513,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"footnotes":""},"categories":[182,170,90,87,180,56,1],"tags":[133,471,96],"acf":[],"yoast_head":"\nMaking the Leap from Data Science Hopeful to Practitioner - insideBIGDATA<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/insidebigdata.com\/2017\/03\/09\/making-leap-data-science-hopeful-practitioner\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Making the Leap from Data Science Hopeful to Practitioner - insideBIGDATA\" \/>\n<meta property=\"og:description\" content=\"It\u2019s a familiar dilemma. You\u2019ve done your research, read some books, taken some online classes \u2013 and at long last, you\u2019re finally ready to get real-life work experience as a Data Scientist. In this contributed article, Dan Saber, Data Science Hiring Manager at Coursera, offers three important steps for successfully transitioning into a data science career.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/insidebigdata.com\/2017\/03\/09\/making-leap-data-science-hopeful-practitioner\/\" \/>\n<meta property=\"og:site_name\" content=\"insideBIGDATA\" \/>\n<meta property=\"article:publisher\" content=\"http:\/\/www.facebook.com\/insidebigdata\" \/>\n<meta property=\"article:published_time\" content=\"2017-03-09T13:00:56+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2017-03-10T16:38:50+00:00\" \/>\n<meta property=\"og:image\" content=\"http:\/\/insidebigdata.com\/wp-content\/uploads\/2017\/03\/dan_saber.jpg\" \/>\n<meta name=\"author\" content=\"Editorial Team\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@insideBigData\" \/>\n<meta name=\"twitter:site\" content=\"@insideBigData\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Editorial Team\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"5 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/insidebigdata.com\/2017\/03\/09\/making-leap-data-science-hopeful-practitioner\/\",\"url\":\"https:\/\/insidebigdata.com\/2017\/03\/09\/making-leap-data-science-hopeful-practitioner\/\",\"name\":\"Making the Leap from Data Science Hopeful to Practitioner - insideBIGDATA\",\"isPartOf\":{\"@id\":\"https:\/\/insidebigdata.com\/#website\"},\"datePublished\":\"2017-03-09T13:00:56+00:00\",\"dateModified\":\"2017-03-10T16:38:50+00:00\",\"author\":{\"@id\":\"https:\/\/insidebigdata.com\/#\/schema\/person\/2949e412c144601cdbcc803bd234e1b9\"},\"breadcrumb\":{\"@id\":\"https:\/\/insidebigdata.com\/2017\/03\/09\/making-leap-data-science-hopeful-practitioner\/#breadcrumb\"},\"inLanguage\":\"en-US\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/insidebigdata.com\/2017\/03\/09\/making-leap-data-science-hopeful-practitioner\/\"]}]},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/insidebigdata.com\/2017\/03\/09\/making-leap-data-science-hopeful-practitioner\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https:\/\/insidebigdata.com\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Making the Leap from Data Science Hopeful to Practitioner\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/insidebigdata.com\/#website\",\"url\":\"https:\/\/insidebigdata.com\/\",\"name\":\"insideBIGDATA\",\"description\":\"Your Source for AI, Data Science, Deep Learning & Machine Learning Strategies\",\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/insidebigdata.com\/?s={search_term_string}\"},\"query-input\":\"required name=search_term_string\"}],\"inLanguage\":\"en-US\"},{\"@type\":\"Person\",\"@id\":\"https:\/\/insidebigdata.com\/#\/schema\/person\/2949e412c144601cdbcc803bd234e1b9\",\"name\":\"Editorial Team\",\"image\":{\"@type\":\"ImageObject\",\"inLanguage\":\"en-US\",\"@id\":\"https:\/\/insidebigdata.com\/#\/schema\/person\/image\/\",\"url\":\"https:\/\/secure.gravatar.com\/avatar\/e137ce7ea40e38bd4d25bb7860cfe3e4?s=96&d=mm&r=g\",\"contentUrl\":\"https:\/\/secure.gravatar.com\/avatar\/e137ce7ea40e38bd4d25bb7860cfe3e4?s=96&d=mm&r=g\",\"caption\":\"Editorial Team\"},\"sameAs\":[\"http:\/\/www.insidebigdata.com\"],\"url\":\"https:\/\/insidebigdata.com\/author\/editorial\/\"}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Making the Leap from Data Science Hopeful to Practitioner - insideBIGDATA","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/insidebigdata.com\/2017\/03\/09\/making-leap-data-science-hopeful-practitioner\/","og_locale":"en_US","og_type":"article","og_title":"Making the Leap from Data Science Hopeful to Practitioner - insideBIGDATA","og_description":"It\u2019s a familiar dilemma. You\u2019ve done your research, read some books, taken some online classes \u2013 and at long last, you\u2019re finally ready to get real-life work experience as a Data Scientist. In this contributed article, Dan Saber, Data Science Hiring Manager at Coursera, offers three important steps for successfully transitioning into a data science career.","og_url":"https:\/\/insidebigdata.com\/2017\/03\/09\/making-leap-data-science-hopeful-practitioner\/","og_site_name":"insideBIGDATA","article_publisher":"http:\/\/www.facebook.com\/insidebigdata","article_published_time":"2017-03-09T13:00:56+00:00","article_modified_time":"2017-03-10T16:38:50+00:00","og_image":[{"url":"http:\/\/insidebigdata.com\/wp-content\/uploads\/2017\/03\/dan_saber.jpg"}],"author":"Editorial Team","twitter_card":"summary_large_image","twitter_creator":"@insideBigData","twitter_site":"@insideBigData","twitter_misc":{"Written by":"Editorial Team","Est. reading time":"5 minutes"},"schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/insidebigdata.com\/2017\/03\/09\/making-leap-data-science-hopeful-practitioner\/","url":"https:\/\/insidebigdata.com\/2017\/03\/09\/making-leap-data-science-hopeful-practitioner\/","name":"Making the Leap from Data Science Hopeful to Practitioner - insideBIGDATA","isPartOf":{"@id":"https:\/\/insidebigdata.com\/#website"},"datePublished":"2017-03-09T13:00:56+00:00","dateModified":"2017-03-10T16:38:50+00:00","author":{"@id":"https:\/\/insidebigdata.com\/#\/schema\/person\/2949e412c144601cdbcc803bd234e1b9"},"breadcrumb":{"@id":"https:\/\/insidebigdata.com\/2017\/03\/09\/making-leap-data-science-hopeful-practitioner\/#breadcrumb"},"inLanguage":"en-US","potentialAction":[{"@type":"ReadAction","target":["https:\/\/insidebigdata.com\/2017\/03\/09\/making-leap-data-science-hopeful-practitioner\/"]}]},{"@type":"BreadcrumbList","@id":"https:\/\/insidebigdata.com\/2017\/03\/09\/making-leap-data-science-hopeful-practitioner\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Home","item":"https:\/\/insidebigdata.com\/"},{"@type":"ListItem","position":2,"name":"Making the Leap from Data Science Hopeful to Practitioner"}]},{"@type":"WebSite","@id":"https:\/\/insidebigdata.com\/#website","url":"https:\/\/insidebigdata.com\/","name":"insideBIGDATA","description":"Your Source for AI, Data Science, Deep Learning & Machine Learning Strategies","potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/insidebigdata.com\/?s={search_term_string}"},"query-input":"required name=search_term_string"}],"inLanguage":"en-US"},{"@type":"Person","@id":"https:\/\/insidebigdata.com\/#\/schema\/person\/2949e412c144601cdbcc803bd234e1b9","name":"Editorial Team","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/insidebigdata.com\/#\/schema\/person\/image\/","url":"https:\/\/secure.gravatar.com\/avatar\/e137ce7ea40e38bd4d25bb7860cfe3e4?s=96&d=mm&r=g","contentUrl":"https:\/\/secure.gravatar.com\/avatar\/e137ce7ea40e38bd4d25bb7860cfe3e4?s=96&d=mm&r=g","caption":"Editorial Team"},"sameAs":["http:\/\/www.insidebigdata.com"],"url":"https:\/\/insidebigdata.com\/author\/editorial\/"}]}},"jetpack_featured_media_url":"","jetpack_shortlink":"https:\/\/wp.me\/p9eA3j-4vO","jetpack-related-posts":[{"id":5926,"url":"https:\/\/insidebigdata.com\/2013\/11\/18\/new-online-degree-program-data-science-udacity\/","url_meta":{"origin":17348,"position":0},"title":"Udacity Launches Data Science Degree Track","date":"November 18, 2013","format":false,"excerpt":"The online education startup Udacity has announced a new data science degree track program that will allow anyone with basic computer science skills to get an education in data science by the companies that helped pioneer the field.","rel":"","context":"In "Education \/ Training"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2013\/11\/udacity.jpg?resize=350%2C200","width":350,"height":200},"classes":[]},{"id":5004,"url":"https:\/\/insidebigdata.com\/2013\/10\/21\/extreems-mathematics-initiative-takes-dead-aim-big-data\/","url_meta":{"origin":17348,"position":1},"title":"EXTREEMS Mathematics Initiative Focuses on Big Data","date":"October 21, 2013","format":false,"excerpt":"The new EXTREEMS interdisciplinary approach at the College of William and Mary will allow undergraduate mathematics majors to gain experience in manipulating data across other discipline.","rel":"","context":"In "Academic"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/www.nist.gov\/tpo\/images\/college-of-william-and-mary_1.jpg?resize=350%2C200","width":350,"height":200},"classes":[]},{"id":14483,"url":"https:\/\/insidebigdata.com\/2016\/02\/16\/datacamp-launches-groups-capability-to-bring-data-science-skills-to-people-worldwide\/","url_meta":{"origin":17348,"position":2},"title":"DataCamp Launches Groups Capability to Bring Data Science Skills to People Worldwide","date":"February 16, 2016","format":false,"excerpt":"DataCamp, the online data science school, has closed an additional $1 million seed funding round from Accomplice. The money will be used to open a new office in Cambridge, expand the team and launch the new Groups functionality.","rel":"","context":"In "Data Science"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":13436,"url":"https:\/\/insidebigdata.com\/2015\/07\/27\/advanced-degrees-for-data-science-predictive-analytics-and-big-data\/","url_meta":{"origin":17348,"position":3},"title":"Advanced Degrees for Data Science, Predictive Analytics and Big Data","date":"July 27, 2015","format":false,"excerpt":"In this special feature, Daniel D. Gutierrez, Managing Editor of insideBIGDATA discusses a question on many people's minds these days - is an advanced degree required to become a data scientist and if so, what programs are available?","rel":"","context":"In "Data Science"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":16039,"url":"https:\/\/insidebigdata.com\/2016\/09\/17\/16039\/","url_meta":{"origin":17348,"position":4},"title":"Guide to Data Science Interviews","date":"September 17, 2016","format":false,"excerpt":"Our friends over at Springboard just released a compelling new infographic that highlights some salary metrics with different data professional roles.","rel":"","context":"In "Data Science"","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2013\/10\/datascienceman.jpg?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":12879,"url":"https:\/\/insidebigdata.com\/2015\/03\/16\/open-source-software-fuels-a-revolution-in-data-science\/","url_meta":{"origin":17348,"position":5},"title":"Open Source Software Fuels a Revolution in Data Science","date":"March 16, 2015","format":false,"excerpt":"In this special guest feature,\u00a0Neera Talbert of Revolution Analytics discusses the role of open source software in making data science the rising field it is today. Neera Talbert is Vice President, Professional Services at Revolution Analytics, a statistical software company focused on developing open source and \"open-core\" versions of the\u2026","rel":"","context":"In "Data Science"","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]}],"_links":{"self":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/posts\/17348"}],"collection":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/users\/10513"}],"replies":[{"embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/comments?post=17348"}],"version-history":[{"count":0,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/posts\/17348\/revisions"}],"wp:attachment":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/media?parent=17348"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/categories?post=17348"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/tags?post=17348"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}