{"id":22663,"date":"2019-05-23T08:30:57","date_gmt":"2019-05-23T15:30:57","guid":{"rendered":"https:\/\/insidebigdata.com\/?p=22663"},"modified":"2019-05-24T08:57:25","modified_gmt":"2019-05-24T15:57:25","slug":"the-data-talent-market-continues-its-ascent","status":"publish","type":"post","link":"https:\/\/insidebigdata.com\/2019\/05\/23\/the-data-talent-market-continues-its-ascent\/","title":{"rendered":"The Data Talent Market Continues Its Ascent"},"content":{"rendered":"\n<p style=\"text-align:center\"><em>Sponsored Post<\/em><\/p>\n\n\n\n<p>In addition to being a practicing data science consultant and journalist, I also play the role of educator. I\u2019m currently teaching two university classes in data science, and as I look out to my classroom packed with new learners I think about the timing of their entry into the field. There could not be a better confluence of a factors leading to likely success in a professional endeavor. We\u2019re at an important inflection point in history where a glaring shortage of data-centric skills, coupled with an <a href=\"https:\/\/insidebigdata.com\/2019\/02\/02\/data-scientist-is-still-a-hot-job-and-pays-well-too\/\">increasing demand for data professionals<\/a>, represents a unique opportunity for those willing to make a commitment to \u201ctool up\u201d or \u201cretool\u201d as the case may be, in preparation for a career in analytics.<\/p>\n\n\n\n<p>The good\nthing is, after all the time and effort, the newly acquired skills will keep on\ngiving because the analytics field shall continue to be in favor for a very\nlong time. The field has tremendous longevity as organizations continue to\ncollect more and more data and increasingly use it as a competitive advantage.<\/p>\n\n\n\n<p><strong>Economic Growth vs. Talent Shortfall<\/strong> <\/p>\n\n\n\n<p>Advancements in AI, data science, and analytics have created unparalleled opportunities, currently driving billions of dollars in economic value, with a projected US$15.7 trillion contribution to global GDP by 2030, as reported by Correlation One\u2019s 2019 global <em><a href=\"https:\/\/insidebigdata.com\/white-paper\/future-of-data-talent-report\/\" target=\"_blank\" rel=\"noreferrer noopener\" aria-label=\"Future of Data Talent (opens in a new tab)\">Future of Data Talent<\/a><\/em> report. This growth has created new challenges as companies struggle to build data teams to execute on their data strategies and goals. Undifferentiated talent classifications (e.g. data scientist vs. data engineer), poor role definitions, antiquated methods of talent assessment, and the continued search for the perfect unicorn, all play a role in the data talent shortfall experienced by many organizations. <\/p>\n\n\n\n<div class=\"wp-block-image\"><figure class=\"aligncenter\"><img decoding=\"async\" loading=\"lazy\" width=\"711\" height=\"385\" src=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/05\/Image1.png\" alt=\"\" class=\"wp-image-22664\" srcset=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/05\/Image1.png 711w, https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/05\/Image1-150x81.png 150w, https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/05\/Image1-300x162.png 300w\" sizes=\"(max-width: 711px) 100vw, 711px\" \/><figcaption>Data technologies are projected to contribute $15.7<br> Trillion to global GDP by 2030. Future of Data Talent 2019 Annual Report by Correlation One<\/figcaption><\/figure><\/div>\n\n\n\n<p>The\ndata science and analytics talent shortfall is massive and getting bigger, a shortage that can hamstring companies for the\nnext decade or longer:<\/p>\n\n\n\n<ul><li>40% of companies claim they are\n     unable to hire or retain data talent due to a lack of supply.<\/li><li>There is expected to be to another\n     2.7M new data-related job postings in the United States by 2020.<\/li><li>There is an expected 20% increase\n     in demand for data talent by 2020.<\/li><\/ul>\n\n\n\n<p><strong>Data Literacy is King<\/strong><strong><\/strong><\/p>\n\n\n\n<p>Even though there is a strong demand for data professionals, there\nis actually an overabundance of people seeking analytics jobs. The problem is\ndata literacy and a lack of emerging workers <a href=\"https:\/\/www.pwc.com\/us\/en\/library\/data-science-and-analytics.html\">who\nhave the full skillset employers need<\/a>. Apparently, the interest is\nthere, but the education is not. &nbsp;<\/p>\n\n\n\n<p>It\u2019s becoming apparent that data literacy is becoming essential\nfor businesses to grow. A <a href=\"https:\/\/economicgraph.linkedin.com\/resources\/linkedin-workforce-report-august-2018\" target=\"_blank\" rel=\"noreferrer noopener\">recent LinkedIn study<\/a> found that the U.S. has a\nshortage of more than 150,000 people with data science skills, especially in\nmajor tech hubs like New York, San Francisco and Los Angeles. With the demand\nfor talent exceeding supply by up to 50%, increased data literacy is of\ncritical importance moving forward. What\u2019s needed are quality degree programs\nthat will open doors and jump-start careers for eager analytics job seekers. <\/p>\n\n\n\n<p><strong>Being a Data Professional Means Never\nStop Learning<\/strong><\/p>\n\n\n\n<p>But even for\nseasoned data professionals, the road to continued success is not always\nsmooth. Decades ago, a term was coined for a \u201cperception of fraud or phoniness\nby a person experiencing success in a senior business role.\u201d The so-called \u201c<a href=\"https:\/\/studyonline.unsw.edu.au\/blog\/what-is-data-science-impostor-syndrome\">Imposter\nSyndrome<\/a>\u201d is now being discussed as a phenomenon affecting those\nworking in the field of data science. Many of my data colleagues have intimated\nto me their own feelings of inadequacy. The reason is simple, this field is\naccelerating at a mind-numbing pace with new programming languages, data\nplatforms\/frameworks, algorithms and data tools coming on the scene at a pace\nwhere no human can realistically master them all, or even keep their head above\nwater. I can\u2019t think of another field that is advancing at such an accelerating\nrate. &nbsp;<\/p>\n\n\n\n<p>Data\nprofessionals are looking for ways to keep up. For example, I write a monthly\narticle that offers the \u201c<a href=\"https:\/\/insidebigdata.com\/2019\/04\/09\/best-of-arxiv-org-for-ai-machine-learning-and-deep-learning-march-2019\/\">Best\nof arXiv.org<\/a>,\u201d the infamous pre-print server for research papers.\nThese articles consistently rank very high in terms of popularity. Why?\nImposter syndrome is the culprit. Many data people value a curated list of\npapers so they can feel like they\u2019re keeping pace with the leading edge of the\nfield. <\/p>\n\n\n\n<p>But the more\nnew books you consume, the more blogs you frequent, the more conferences you\nattend, the more webinars you watch \u2013 the more many data professional have that\nsinking feeling about what they don\u2019t know. It\u2019s a never ending feedback loop\nthat inserts a degree of uncertainty in people\u2019s professional lives. But there\nare ways to mitigate these feelings of being an imposter. As many of today\u2019s\ndata professionals have transitioned into the field from other disciplines, one\nimportant strategy with which to overcome this syndrome is to solidify your\nacademic background with a formal degree program in a data related field. A\ndegree advances your street cred to a competitive level with your\ncontemporaries and at the same time improves your confidence that you know as\nmuch as most others. <\/p>\n\n\n\n<p><strong>Core Ingredients of a Data Degree\nProgram<\/strong><\/p>\n\n\n\n<p>What can you expect to gain from a <a href=\"https:\/\/studyonline.unsw.edu.au\/online-programs\/master-analytics\" target=\"_blank\" rel=\"noreferrer noopener\" aria-label=\" (opens in a new tab)\">postgraduate degree program in analytics<\/a>? Such programs can propel you into the analytics field by providing foundation subjects as programming, data analysis, and analytics for business. In addition, you can master core subjects like managing data teams, data visualization, data storytelling, and predictive analytics. Supplemental subjects may include decision making and financial modeling. Lastly, and critically important today, is a degree program with a solid focus on the ethical use of data and combating algorithmic bias. Many degree programs take <a href=\"https:\/\/www.forbes.com\/sites\/kalevleetaru\/2018\/10\/23\/even-the-data-ethics-initiatives-dont-want-to-talk-about-data-ethics\/#11c477171fba\">shortcuts when it comes to data ethics<\/a>, but most agree that this situation must change and give attention to where data comes from, what can be done with it and whether it can be actively or only passively collected. <\/p>\n\n\n\n<p><strong>Conclusion<\/strong><\/p>\n\n\n\n<p>To anyone feeling excitement about entering the data industry, your time is now. This \u201ccandidate\u2019s market\u201d won\u2019t last forever as more people acquire the skills necessary to land a job in the field. In order to take advantage of this market inefficiency, all you need to do is make a firm commitment to the field, and acquire the skills employers are seeking to fill an ever increasing number of analytics job positions. If you don\u2019t have the proper academic background, or even if you want to supplement your existing academic history with a fresh outlook, it\u2019s time to seek out a shiny new degree that can help pave your way to success as an analytics professional.<\/p>\n\n\n\n<div class=\"wp-block-image\"><figure class=\"alignleft is-resized\"><img decoding=\"async\" loading=\"lazy\" src=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2018\/12\/Daniel_2018_pic.png\" alt=\"\" class=\"wp-image-21778\" width=\"133\" height=\"152\" srcset=\"https:\/\/insidebigdata.com\/wp-content\/uploads\/2018\/12\/Daniel_2018_pic.png 200w, https:\/\/insidebigdata.com\/wp-content\/uploads\/2018\/12\/Daniel_2018_pic-131x150.png 131w\" sizes=\"(max-width: 133px) 100vw, 133px\" \/><\/figure><\/div>\n\n\n\n<p><em>Contributed by Daniel D. Gutierrez, Managing Editor and Resident \nData Scientist for insideBIGDATA. In addition to being a tech \njournalist, Daniel also is a consultant in data scientist, author, \neducator and sits on a number of advisory boards for various start-up \ncompanies.&nbsp;<\/em><\/p>\n\n\n\n<p><em>Sign up for the free insideBIGDATA&nbsp;<a rel=\"noreferrer noopener\" href=\"http:\/\/insidebigdata.com\/newsletter\/\" target=\"_blank\">newsletter<\/a>.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>We\u2019re at an important inflection point in history where a glaring shortage of data-centric skills, coupled with an increasing demand for data professionals, represents a unique opportunity for those willing to make a commitment to \u201ctool up\u201d or \u201cretool\u201d as the case may be, in preparation for a career in analytics. The good thing is, after all the time and effort, the newly acquired skills will keep on giving because the analytics field shall continue to be in favor for a very long time. <\/p>\n","protected":false},"author":37,"featured_media":22602,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"footnotes":""},"categories":[65,115,182,87,180,82,56,97,1],"tags":[314,133,430,471,95],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v20.6 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>The Data Talent Market Continues Its Ascent - 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\/2019\/05\/23\/the-data-talent-market-continues-its-ascent\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"The Data Talent Market Continues Its Ascent - insideBIGDATA\" \/>\n<meta property=\"og:description\" content=\"We\u2019re at an important inflection point in history where a glaring shortage of data-centric skills, coupled with an increasing demand for data professionals, represents a unique opportunity for those willing to make a commitment to \u201ctool up\u201d or \u201cretool\u201d as the case may be, in preparation for a career in analytics. 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Gutierrez is a Data Scientist with Los Angeles-based AMULET Analytics, a service division of AMULET Development Corp. He's been involved with data science and Big Data long before it came in vogue, so imagine his delight when the Harvard Business Review recently deemed \\\"data scientist\\\" as the sexiest profession for the 21st century. Previously, he taught computer science and database classes at UCLA Extension for over 15 years, and authored three computer industry books on database technology. He also served as technical editor, columnist and writer at a major computer industry monthly publication for 7 years. 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Gutierrez is a Data Scientist with Los Angeles-based AMULET Analytics, a service division of AMULET Development Corp. He's been involved with data science and Big Data long before it came in vogue, so imagine his delight when the Harvard Business Review recently deemed \"data scientist\" as the sexiest profession for the 21st century. Previously, he taught computer science and database classes at UCLA Extension for over 15 years, and authored three computer industry books on database technology. He also served as technical editor, columnist and writer at a major computer industry monthly publication for 7 years. Follow his data science musings at @AMULETAnalytics.","sameAs":["http:\/\/www.insidebigdata.com","https:\/\/twitter.com\/@AMULETAnalytics"],"url":"https:\/\/insidebigdata.com\/author\/dangutierrez\/"}]}},"jetpack_featured_media_url":"https:\/\/insidebigdata.com\/wp-content\/uploads\/2019\/05\/AI_Job_SHUTTERSTOCK.jpg","jetpack_shortlink":"https:\/\/wp.me\/p9eA3j-5Tx","jetpack-related-posts":[{"id":17717,"url":"https:\/\/insidebigdata.com\/2017\/04\/26\/new-research-proves-increased-awareness-value-open-data-science-enterprises-slow-respond\/","url_meta":{"origin":22663,"position":0},"title":"New Research Proves Increased Awareness in the Value of Open Data Science, but Enterprises are Slow to Respond","date":"April 26, 2017","format":false,"excerpt":"New research announced by Continuum Analytics, the creator and driving force behind Anaconda, a leading Open Data Science platform powered by Python, finds that 96 percent of data science and analytics decision makers agree that data science is critical to the success of their business, yet a whopping 22 percent\u2026","rel":"","context":"In &quot;Big Data&quot;","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":23779,"url":"https:\/\/insidebigdata.com\/2020\/01\/03\/ultimate-guide-to-cleaning-data-with-excel-and-google-sheets\/","url_meta":{"origin":22663,"position":1},"title":"Ultimate Guide to Cleaning Data with Excel and Google Sheets","date":"January 3, 2020","format":false,"excerpt":"The \"Ultimate Guide to Cleaning Data with Excel and Google Sheets\" eBook by Inzata Analytics discusses how poor data quality is the kryptonite of good reporting and credible analytics. Managing and ensuring data is clean can provide significant value to any business. This guide details useful steps and tips to\u2026","rel":"","context":"In &quot;Big Data&quot;","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2019\/12\/Inzata_Guide_Cleaning_Data.png?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":18041,"url":"https:\/\/insidebigdata.com\/2017\/06\/03\/difference-data-science-data-analytics\/","url_meta":{"origin":22663,"position":2},"title":"The Difference Between Data Science and Data Analytics","date":"June 3, 2017","format":false,"excerpt":"In this contributed article, tech writer Rick Delgado, examines the differences between the terms: data science and data analytics, where people working in the tech field or other related industries probably hear these terms all the time, often interchangeably. Although they may sound similar, the terms are often quite different\u2026","rel":"","context":"In &quot;Data Science&quot;","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":21794,"url":"https:\/\/insidebigdata.com\/2019\/01\/17\/mit-xpros-online-data-science-course\/","url_meta":{"origin":22663,"position":3},"title":"Ask a TA: Everything You Need to Know About MIT xPRO\u2019s Online Data Science Course","date":"January 17, 2019","format":false,"excerpt":"Eric Bradford, a Teaching Assistant for MIT xPRO\u2019s Online Data Science Course, Data Science and Big Data Analytics: Making Data-Driven Decisions, gives his insights into the unique seven-week course. The seven-week course explores the theory and practice behind recommendation engines, regressions, network and graphical modeling, anomaly detection, hypothesis testing, machine\u2026","rel":"","context":"In &quot;Academic&quot;","img":{"alt_text":"","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2019\/01\/Unknown-e1546453798393.jpeg?resize=350%2C200&ssl=1","width":350,"height":200},"classes":[]},{"id":12986,"url":"https:\/\/insidebigdata.com\/2015\/04\/13\/data-science-101-excel-tutorial-on-analyzing-large-data-sets\/","url_meta":{"origin":22663,"position":4},"title":"Data Science 101: Excel Tutorial on Analyzing Large Data Sets","date":"April 13, 2015","format":false,"excerpt":"Our friends over at Udemy partnered with data scientist David Taylor (specialist in data spelunking and visualization) to create a fun (and free) Excel tutorial on analyzing large data sets.","rel":"","context":"In &quot;Big Data&quot;","img":{"alt_text":"boyslast","src":"https:\/\/i0.wp.com\/insidebigdata.com\/wp-content\/uploads\/2015\/04\/boyslast.gif?resize=350%2C200","width":350,"height":200},"classes":[]},{"id":12583,"url":"https:\/\/insidebigdata.com\/2015\/01\/10\/data-science-101-lessons-kaggle-competitions\/","url_meta":{"origin":22663,"position":5},"title":"Data Science 101: Lessons Learned from Kaggle Competitions","date":"January 10, 2015","format":false,"excerpt":"In the video presentation below, \"Machine learning best practices we've learned from hundreds of competitions,\" Ben Hamner, Chief Scientist at Kaggle, discusses some very intriguing insights into how find success in data science projects.","rel":"","context":"In &quot;Data Science&quot;","img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]}],"_links":{"self":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/posts\/22663"}],"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\/37"}],"replies":[{"embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/comments?post=22663"}],"version-history":[{"count":0,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/posts\/22663\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/media\/22602"}],"wp:attachment":[{"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/media?parent=22663"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/categories?post=22663"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/insidebigdata.com\/wp-json\/wp\/v2\/tags?post=22663"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}