<p><div><div><div><div data-width="100" data-show-faces="false" data-send="true" data-layout="button_count" data-height="21" data-href="https://www.r-bloggers.com/so-you-want-to-be-a-data-scientist/"></div></div><div><a data-url="https://www.r-bloggers.com/so-you-want-to-be-a-data-scientist/" data-counturl="https://www.r-bloggers.com/so-you-want-to-be-a-data-scientist/" data-count="horizontal" data-text="So you want to be a data scientist" class="twitter-share-button" data-via="rbloggers" href="https://twitter.com/share"></a></div><div></div></div><div></div></div>
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(This article was first published on <strong><a href="http://ekonometrics.blogspot.com/2016/08/so-you-want-to-be-data-scientist.html"> eKonometrics</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
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<div>From <a target="_blank" href="http://www.huffingtonpost.ca/murtaza-haider/data-scientist-career_b_11426570.html">HuffingtonPost</a></div>
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<div>The <i>New York Times</i> made it look so easy. Take a few courses in data science and a web-based startup will readily pay top dollars for your newly acquired skills.</div>
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<div><span lang="EN-CA">Since the <a target="_blank" href="http://www.mckinsey.com/business-functions/business-technology/our-insights/big-data-the-next-frontier-for-innovation"><span lang="EN-US">McKinsey Global Institute</span></a> reported on the impending shortage of data crunchers, the wanna be data scientists are searching for learning opportunities in big data analytics. Newspaper coverage suggests that even with limited previous exposure to empirics, one may enroll in <a target="_blank" href="https://en.wikipedia.org/wiki/Massive_open_online_course"><span lang="EN-US">MOOCs </span></a>or join programming boot camps to establish one’s bonafides.</span></div>
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<div><span lang="EN-CA">In a recent blog on <a target="_blank" href="http://www.forbes.com/sites/metabrown/2016/07/29/4-reasons-not-to-get-that-masters-in-data-science/#5a484391694f"><span lang="EN-US">Forbes.com</span></a>, Meta S. Brown, the author of <a target="_blank" href="https://www.amazon.ca/Data-Mining-Dummies-Meta-Brown/dp/1118893174"><i><span lang="EN-US">Data Mining for Dummies</span></i></a>, gave four reasons not to get an advanced degree in data science. I, on the other hand, believe that a structured learning environment is exactly what many need to enable the career change they have contemplated for years but have not moved on it.</span></div>
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<div><span lang="EN-CA">It all depends on upon what kind of a learner you are. If you are a disciplined, self-motivated, self-actuated individual, you can pick up the skills by attending MOOCs or participating in coding boot camps.</span></div>
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<div><span lang="EN-CA">But if you are like the rest of us, who once enrolled in a free online course, but didn’t complete it, you need some structure. A degree or a certificate in data science or business analytics is exactly what you need to upgrade your skills and be part of the network that will help you reorient your career.</span></div>
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<div><span lang="EN-CA">In my book, <a target="_blank" href="http://www.ibmpressbooks.com/store/getting-started-with-data-science-making-sense-of-data-9780133991024"><i><span lang="EN-US">Getting Started with Data Science</span></i></a>, I mentioned <a target="_blank" href="http://www.nytimes.com/2015/07/29/technology/code-academy-as-career-game-changer.html?_r=0"><span lang="EN-US">Paul Minton</span></a>, who was making $20,000 serving tables in New York. However, a three-moth programming course at the <a target="_blank" href="http://www.zipfianacademy.com/"><span lang="EN-US">Zipfian Academy</span></a> turned his life around. He earned over $100,000 in 2014 as a data scientist for a web startup in San Francisco. “Six figures, right off the bat … To me, it was astonishing,” he told <i>The New York Times</i>.</span></div>
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<div><a target="_blank" href="https://i0.wp.com/4.bp.blogspot.com/-XGu9G1Ng-As/V6ufPZ1gRGI/AAAAAAAADj4/LqdQjEdCcLgtQbBzu9xg1FnZd_7m6F5lQCLcB/s1600/Murtaza%2BHaider%2BGetting%2Bstarted%2Bcover.jpg?ssl=1"><img src="https://i1.wp.com/4.bp.blogspot.com/-XGu9G1Ng-As/V6ufPZ1gRGI/AAAAAAAADj4/LqdQjEdCcLgtQbBzu9xg1FnZd_7m6F5lQCLcB/s320/Murtaza%2BHaider%2BGetting%2Bstarted%2Bcover.jpg?resize=213%2C320&ssl=1" border="0" data-recalc-dims="1"></a></div>
<div>When the inspiring data scientists think of a career in the ‘glamorous’ world of big data and analytics, they think of Mr. Minton. His story, though a bit Cinderella-ish, is true, but rare. He works for <a target="_blank" href="http://www.change.org/"><span lang="EN-US">Change.org</span></a>! However, not everyone should expect a similar outcome. In addition to good fortune, Mr. Minton had majored in math in his undergraduate training, and we all know that math helps. It will be unwise, however, to assume that with almost no  empirical background, one can master the </div>
<div><span lang="EN-CA">complex world of data and algorithms in a matter of a few weeks and be gainfully employed.</span></div>
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<div><span lang="EN-CA">While speaking at meet-ups organized by IBM’s <a target="_blank" href="https://bigdatauniversity.com/"><span lang="EN-US">BigDataUniversity</span></a>, I encounter dozens of enthusiasts who are keen to start training in data science but do not know where to begin. I advise them to build on their core competencies and domain knowledge. For instance, if you studied journalism or creative writing as an undergraduate, you might want to learn how to analyze socioeconomic data instead of trying to set up <a target="_blank" href="http://www.ibm.com/analytics/ca/en/technology/hadoop/?S_TACT=M1610LNW&S_PKG=-&campaign=Unbranded%7CSearch%7CHadoop%20HEAD%20TERM%7CCanada%7CN/A%7C&group=Hadoop_EXACT&mkwid=c6c2fcb6-3098-452f-9d1a-b1ce0f3044ff%7C509%7C&ct=M1610LNW&iio=BANAL&cmp=M1610&ck=hadoop&cs=e&ccy=US&cr=bing&cm=k&cn=Hadoop_EXACT"><span lang="EN-US">Hadoop clusters</span></a>, a big data task best left to computer scientists and engineers.</span></div>
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<div><span lang="EN-CA">If you are a disciplined learner, you can explore data science training offered as MOOCs. <a target="_blank" href="https://www.coursera.org/"><span lang="EN-US">Coursera</span></a>, one of the largest MOOCs platform, listed several data science courses among the top <a target="_blank" href="http://www.businessinsider.com/most-popular-coursera-courses-of-2015-2015-12"><span lang="EN-US">10 most popular courses</span></a> in 2015. IBM’s Big Data University (BDU) is another platform dedicated to promoting training in data science and analytics. Not only BDU offers similar resources for online learning as other platforms, it also offers cloud-based resources for hands-on training through the <a target="_blank" href="https://datascientistworkbench.com/"><span lang="EN-US">Data Scientist Workbench</span></a>.</span></div>
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<div><span lang="EN-CA">The Workbench provides the state-of-the-art computing solutions for regular-sized data. These include <a target="_blank" href="https://www.r-project.org/"><span lang="EN-US">R</span></a>, <a target="_blank" href="https://www.python.org/"><span lang="EN-US">Python</span></a>, and <a target="_blank" href="http://openrefine.org/"><span lang="EN-US">OpenRefine</span></a>. To wrangle big data, the Workbench offers Hadoop and <a target="_blank" href="http://www.ibm.com/analytics/us/en/technology/cloud-data-services/data-scientist/?S_PKG=&S_TACT=M1610NHW&campaign=Branded%7CSearch%7CCloud%20Data%20Services%20For%20Data%20Scientists%7CNA%7CN/A%7C&group=Data_Science-COG%20(Broad)&mkwid=c6c2fcb6-3098-452f-9d1a-b1ce0f3044ff%7C509%7C109165367546&ct=M1610NHW&iio=BANAL&cmp=M1610&ck=ibm%20data%20science&cs=b&ccy=US&cr=google&cm=k&cn=Data_Science-COG%20(Broad)"><span lang="EN-US">Spark</span></a>-based solutions. Such coupling of computing infrastructure with online learning resources frees the new learners from the concerns about installing and maintaining software and clustering hardware.</span></div>
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<div><span lang="EN-CA">For learners who would prefer a structured learning environment, they also have several options. They can register for courses or certificates offered by universities’ <a target="_blank" href="http://learn.utoronto.ca/courses-programs/business-professionals/certificates/management-of-enterprise-data-analytics"><span lang="EN-US">continuing education faculties</span></a>, enroll in an online graduate degree in data science, or take a more traditional approach of enrolling in a full- or part-time <a target="_blank" href="http://www.ryerson.ca/graduate/datascience/index.html"><span lang="EN-US">Master’s program</span></a>.</span></div>
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<div><span lang="EN-CA">A good place to search for learning opportunities is the <a target="_blank" href="http://www.kdnuggets.com/education/usa-canada.html"><span lang="EN-US">KDNuggets </span></a>website that maintains detailed lists of post-graduate programs in data science including full-time, part-time, and online masters and other certifications.</span></div>
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<div><span lang="EN-CA">Once you have earned some credentials, you still have to prove your worth to future employers. If you are making a switch from another career, your experience may not be of much use in your pursuits in the data-centric world. My advice to the novice data scientists lacking experience is to ask the potential employer not necessarily for a job, but instead for a data set and a puzzle. If you can solve a data-oriented problem for a firm as part of the vetting process, you can overcome the shortcomings in your résumé.</span></div>
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<div><span lang="EN-CA">For those who are still on the fence thinking whether to take the plunge into the world of big data and analytics, they should know that the demand for data scientists far exceeds the capacity of the universities and colleges to produce them. This is unlikely to change shortly. Act now and embrace data.</span> </div>
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