{"id":6792,"date":"2020-10-26T15:01:12","date_gmt":"2020-10-26T15:01:12","guid":{"rendered":"https:\/\/data-science.gotoauthority.com\/2020\/10\/26\/aint-no-such-a-thing-as-a-citizen-data-scientist\/"},"modified":"2020-10-26T15:01:12","modified_gmt":"2020-10-26T15:01:12","slug":"aint-no-such-a-thing-as-a-citizen-data-scientist","status":"publish","type":"post","link":"https:\/\/wealthrevelation.com\/data-science\/2020\/10\/26\/aint-no-such-a-thing-as-a-citizen-data-scientist\/","title":{"rendered":"Ain\u2019t No Such a Thing as a Citizen Data Scientist"},"content":{"rendered":"
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By Venkat Raman<\/a>, Data Scientist at True Influence<\/b>.<\/p>\n

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Image credit.<\/em><\/a><\/p>\n

Dear Aspiring Data Scientist,<\/p>\n

Before you start using \u2018low code\u2019 or \u2018drag & drop\u2019 data science tools, please learn the fundamentals.<\/p>\n

Why aspire to be a \u2018Citizen Data Scientist\u2019 when you can truly become a \u2018Data Scientist\u2019?<\/p>\n

Don\u2019t get swayed by the fancy titles like \u2018Citizen Data Scientist.\u2019 It is funny that so much hard selling is happening in data science.<\/p>\n

I mean, just because we know how to use a thermometer or operate a BP machine, should we start calling ourselves \u2018Citizen Doctor\u2019?<\/p>\n

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Image credit: KDnuggets.com<\/em><\/p>\n

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Strategy \u2014 undermine the difficulty of doing data science!<\/h3>\n

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The undermining of difficulty in doing data science is not healthy. Many \u2018become a data scientist in a 1-month course\u2019<\/em>\u00a0sellers and \u2018low code data science solution\u2019<\/em>\u00a0sellers use this strategy.<\/p>\n

The \u2018low code\/no-code solution\u2019 sellers will often argue that one could gain intuition by *doing* things. The counter-argument to that is, using a low code\/no-code solution is like using a calculator. Before one can operate a calculator, one needs to have numeracy skills. Learning the fundamentals in data science is like acquiring numeracy skills.<\/p>\n

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Image credit.<\/em><\/a><\/p>\n

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Why 85% of Data Science projects fail? (hint: No skin in the game)<\/h3>\n

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85% of Data Science projects fail in the enterprise because people think it is easy to do data science but only do it wrongly. The realization often comes late.<\/p>\n

Many fall victim to the \u2018become a data scientist in 1 month\/6 months-type courses\u2019<\/em>\u00a0and often wonder why they are not being hired.<\/p>\n

The market is the ultimate truth-teller.<\/p>\n

It somehow knows who the good players are and operates an excellent filtering mechanism. The reason being, the market is comprised of companies that have \u2018skin in the game.\u2019<\/p>\n

Companies having \u2018skin in the game\u2019 don\u2019t gamble. They hire genuine talent. The simple \u2018skin in the game\u2019 test one can do by themselves is ask one simple question.\u00a0Would I use the machine learning classifier myself?<\/em><\/strong><\/p>\n

I came across a LinkedIn post where a person built a heart disease prediction model using one of the low code libraries. The real question is whether that person would use that model on his\/her kith and kin?<\/p>\n

Also, the real utility of heart disease prediction or earthquake prediction is not the prediction that it will happen with x% certainty, but\u00a0WHEN<\/strong>\u00a0will it happen.<\/p>\n

This \u2018temporal\u2019 part no model can predict accurately.<\/p>\n

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Doing Data Science is easy. Or is it?<\/h3>\n

\u00a0<\/p>\n

One of the reasons data science seems *easy to do* is because many algorithms can be fit in 2\u20133 lines of code. There is simply no intellectual pain.<\/p>\n

Compare this to programming. A person has to think about the syntax, design pattern, and logic. When things go astray in programming, there are multiple checkpoints in the form of error alerts like Runtime, Syntax error, and compiler error. One gets an immediate reality check on how good or bad a programmer he\/she is. As a result, one does not go up and about calling themselves \u2018citizen software engineer.\u2019<\/p>\n

On the flip side, when it comes to data science, there is no runtime or syntax error equivalent. There are no warning signs that says one can\u2019t apply a particular algorithm on the data. There is no immediate reality of check-in data science.<\/p>\n

This is one reason why people who advocate \u2018learning the fundamentals is not important\u2019<\/em>\u00a0go scot-free. This is why fancy but harmful titles like \u2018citizen Data Scientist\u2019 arise.<\/p>\n

The above criticism might sound rude\/bitter, but it is all in the hope that one day we can all say 85% of Data Science projects succeed rather than fail.<\/p>\n

Original<\/a>. Reposted with permission.<\/p>\n

\u00a0<\/p>\n

Related:<\/b><\/p>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

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