ATn the early 2000s, the first real data scientists were mathematicians, physicists and statisticians. These specialists, operating in ultra-siloed departments, manually coded and extracted data to generate business insights. Data science remains a particularly new discipline and it takes time, resources and trainers to build a curriculum from scratch.
This gap between the current technological landscape and the skills taught is a persistent problem. We are living through one of the greatest periods of data generation in history, and companies are increasingly looking to leverage it to make quick and accurate decisions. However, the number of skilled professionals entering or re-entering the workforce is failing to meet the demand for business intelligence and relevant information.
Since businesses need to make more informed decisions, they need to be able to rely on accurate data, and humans remain the most effective way to build these foundations. It’s about putting the right tools in the hands of the right employees—those who not only have the business knowledge, but also understand the context of the problem at hand.
A general misunderstanding
It is probably because of this early visibility in a new field that data scientist has become somewhat synonymous with “data analytics”, a result of a lack of understanding both in the corporate world and among teaching professionals. The result is a general misunderstanding, which suggests that the solution to these data analytics challenges is to hire highly skilled data scientists who can hand-code solutions to drive business value. In fact, learning the code alone will not be able to fill this data analysis skills gap. The solution to this science, technology, engineering, and math (STEM) skills gap does not lie with coders, but rather with experts in other fields who have both curiosity and data literacy.
“If you were lost, you wouldn’t ask directions from a data science team, but from a taxi”
If you were lost, you wouldn’t ask directions from a data science team, but from a taxi. This expertise is irreplaceable. In the same vein, not all learners need to know how to code. Python does not turn an average person into a high-level data analyst, so this computer language should not be a mandatory skill for data analytics. Bringing in collaborators who are experts in a field and training them in accessible analytics tools means that they can work more closely with the data science team and, in many cases, they can solve their own problems on their own. pace.
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