Data science tools of the trade fill skills gap Posted on : Jul 21 - 2018

Data science tools are becoming more intelligent and getting better at understanding intent and context. But don't stop advertising that data scientist job on the web just yet.

The data scientist remains one of the most in-demand jobs, and there are a growing number of data science programs across the country to teach techies the necessary skills. But by the time you gain the skills you need, the job and the data science tools may look much different than they do today.

Data scientist skills vary depending on the company, but they generally involve the ability to identify business needs and formulate problem statements based on business requirements. They can prepare the right data for analysis to help meet business needs and assist company leaders to help make decisions, according to Adrian Bowles, industry analyst and founder of Storm Insights, in a Dataversity webinar called "The Disappearing Data Scientist."

"They can find the right tools, find the right approach to the data, analyze it in a way that makes sense and interpret that," Bowles said in the webinar. "A data scientist is a business storyteller who tells a narrative based on quantitative data."

Bowles likened the demand for data scientists to the need for programmers in the 1950s. By the 1960s and 1970s, there were major changes in the way programming was done.

Programmers haven't gone away -- they are still in high demand -- but the role itself, and the way programming is done, has evolved because the tools and techniques are at a higher level.

The industry has also seen tools being used by business users to generate code, removing some of the need for programming, Bowles said.

"Over the years, the skill level to write software has changed; you don't need to know as much about the underlying machine because everything is at a higher level of abstraction," he said. "The end result, if you measure [it] in terms of productivity and what is being delivered by programmers today, is much higher than what was being delivered by folks that knew more about the underlying structure years ago."

Data science tools augment, automate

Data growth combined with the data scientist shortage has spawned an environment in which executives expect line-of-business users to become adept at using self-service data science tools. On the supply side, AI technologies such as machine learning classification systems and natural language processing are maturing to the point where they can augment business analysis or automate processes, but the quality of augmentation and automation varies and, in some cases, still has a ways to go. View More