With the ever-increasing amount of enterprise information, data scientists are in hot demand, landing first on Glassdoor’s Best Jobs in America list for the past four years in a row. It goes without saying that coding languages, big data analysis, machine learning, algorithms, and other advanced technical skills are essential for career success as a data scientist. But, according to a recent article by TechRepublic’s Macy Bayern, “technical skills alone won’t cut it.”
Emphasizing that communication, collaboration, and constant learning are also critical abilities for data scientists to master, she outlined three common pitfalls for data scientists to avoid.
1. A laser focus on the solution
It’s rare that a data problem is black and white and treating it as such could lead to issues down the road. At the outset of any project, data scientists must do leg work to establish the context in which the problem exists and determine any other systems or applications involved, how they interact with each other, and how the proposed solution might change this dynamic.
2. Forgetting the basics
In the age of artificial intelligence (AI), machine learning, and other advanced technology implementations, data scientists often overlook simple tools that must be applied before more mature technologies can yield any actionable intelligence. One analytics expert interviewed by Bayern described the scenario thusly, “A data scientist who has model building skills without fundamentals is like a pilot who can fly an airplane without knowing what the cockpit dials mean.”
3. Poor communication
Finding analytical results is just one part of a data scientist’s remit. It’s also important that he or she be able to effectively communicate those findings to various groups within the organization. This can be a tough stumbling block, particularly for data scientists who come directly from an academic setting and are used to collaborating solely with others who share a strong background in analytics. In the enterprise environment, however, it’s critical that analytical results be disseminated to an array of users with varying degrees of technical abilities. As such, poor communication is a pitfall data scientists must avoid at all costs.
For more on what practitioners can do to overcome the above challenges, you can read the TechRepublic piece in its entirety here.
And take a look at this recent APEX post on citizen data scientists to see why traditional data scientists aren’t the only ones driving data initiatives forward.