Whether or not we need data scientists are valid concerns today. In other words, Natural Language Processing Languages are breaking down barriers so anyone may perform analytics in IT. What are NPLs? To be concise, they help bring down barriers through conversion of data into our language. Certain NPLs may transform data into an elaborate report. People who lack the IT background can learn the surface of NPLs and still qualify for the positions as a big data scientist. The serious concern we have is that we do need big data scientists no matter what. People who specialize in SQLs, database management, and other IT skills may not stand out in the 21st century. To counter Mr. Marr’s point, I believe NLPs aren’t thinning out the big data scientists’ careers. In fact, NLPs may provide new opportunities for data scientists. From my previous experience, I believe the most effective way to learn something, especially SQLs, is to be exposed to them. To be exposed, you have to be surrounded by working with data as well as NPLs. Technology is indeed improving but it also increases the skills and provides opportunities for data scientists. However, people who don’t have an IT background may have the basic skills of Python, but may not have the ability to perform it well enough. That’s where Natural Processing Languages come in; they analyze the data for the inexperienced user. So why bother hiring a big data analyst when a NPL can lay out the data for you? Therefore, the need for database analysts is decreasing because it is cheaper to hire someone with a lower skill (lay person) set than a higher skill set (database analyst).
There are some benefits of using NPLs. Below is a link to a 12-week program called Fintech Innovation Lab. They are currently partnered with Accenture and Partnership Fund for New York City. In the video, Fintech Innovation Lab that enrolled several local businesses and helped these firms to get licensed in their program. Through their program, this allows employees as well as employers to better familiarize themselves with NPLs, such as Python. Although it sounds like an excellent pitch, there are some downsides to these sort of programs. For instance, the program may also not teach the trainees to think analytically and independently. Also, we cannot expect these trainees to progress to upper level management since they lack in the basic analytical skills that a big data scientist may have. In the short run, it’s a fantastic plan, but there will be many problems in the long run, such as management and teaching their employees how to use SQLs and other database management skills. In addition, the lay people may have to re-familiarize and train themselves again, which will waste resources as well as time.