Preparing to Get Qualified as a Data Scientist: Be Ready for Change
How does one go about training to become a data scientist?
One of the sources most cited for practical direction in pursuing a career in data science is a KDnuggets article that was originally published in 2014 but was regularly updated and expanded through 2018, though it retains the number of the original title. So 9 Must-have skills you need to become a Data Scientist, in fact, lists 13 skills, though whether or not all should count as “skills” is debatable.
KDnuggets’ must-have skills begins with formal education in terms of university degrees. The article points out that the majority of data scientists possess advanced degrees: 46% have PhDs, and 88% hold at least a master’s-level degree. They also build up their core skills at the undergraduate level. The most popular choice for this career track is a bachelor’s in math and statistics, which makes up about a third. The next most popular is a computer science degree, which is held by 19%. The third choice, which makes up 16%, is engineering.
Any of those choices would contribute to requisite skills for the field, though some shift into it from hard sciences or even from the arts. Certainly, a number of students at the NYC Data Science Academy enter the program with a degree in another field and get up to speed on coding and math prior to taking the plunge into the data science focus. Such students already understand the necessity of learning new skills to adapt to the needs of the workplace.
As the coding and other technical skills that data scientists need to know can vary over time — something we will look into a bit further on — a data scientist must above all retain the motivation and adaptability to acquire new skills and languages. Given the rapid pace of technology, the techniques involved in data science seven years down the road will look quite different from the ones that are currently in use.
This kind of change is inevitable, according to a recent article on what is involved in staying “relevant in the future of work.” In contrast to the old normal in which people would qualify for their professions at the beginning of their careers and then just keep doing the same thing, people who want to stay in the game tomorrow will have to keep learning new skills. “The half-life of a skill has dropped from 30 years to an average of 6 years,” it explains.
What that means for those who are currently seeking to get qualified is that they should not expect to be done with their training ever after. The old normal of “learn at school and do at work” is no longer sustainable in the corporate world. It’s not just a matter of getting ahead — but just of survival — to learn new technology and processes in order to keep up with the changes at work.
Given the reality of today’s world, the education of a data scientist must encompass more than obtaining a degree in computer science and a certificate in data science or taking the courses for the various tools used in the profession. It’s a matter of learning how to approach problems like a data scientist and then using the various tools available to obtain the best insight and models to suit an organization’s goals. Keeping on top of the game will require keeping up with new techniques that emerge.
But you still will have to begin with a core list of skills, and we will look at that in the next section.