Man vs Machine (Learning): The Data Scientist is not Obsolete

Man vs Machine (Learning): The Data Scientist is not Obsolete

There is a debate raging. Experts are talking and chat rooms are buzzing – Will machine learning algorithms and AI effectively end the reign of the Data Scientist? This is one debate that Data Scientists everywhere are keenly watching as it does, after all, concern their jobs and careers. Most of them however, watch with a smile on their lips as they know , as we do, that the human element in Data Science may evolve and mature, be redefined and innovated, but no matter what, will never really be put to death.

Indeed these past few years we have witnessed the demand for data savvy professionals grow at an electrifying and phenomenal pace and we have seen companies willing to pay big bucks for the right talent.We have also at the same time seen the emergence of bigger and better Data Science machines and so rightly enough they are some in the industry who wonder if there will come a time when the human interface so vital to Data Science will no longer be necessary. I honestly don’t think so and here are themain reasons why:

  1. For Data analytics to be successful, itneeds context and interpretation,people who understand how the models work,and because only they can establish whichinsightsamong the many derived, aremost useful.
  2. Data scientists will still be needed to input the coding that creates the algorithms used for the analyses. We will need humans to determine the specific features and data sets that will be used for effective and relevant data analysis and visualization.

While it’s true that machines will become smarter and we will see them perhaps even doing a lot of what today’s data scientists are doing, what we must remember is that, at the same time, the data science industry will continue evolving and inventing tools and techniques, all of which will need human interface. So the key to success for a professional looking forward to a long and successful career in data science is to continuously keep updated with the latest skills in demand. Don’t be afraid to re-invent yourself if necessary, because the only given that we know is that things will change and we must change with it.

Let’snow take a look at the image below to try and understand the various layers of human intervention in present day data science. You will see that in a typical Big Data team, each member of the team has a specific function. Though each role is clearly defined, only by working collaboratively together, are insights garnered and the larger goals of the organization realized.

 

 

 
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