“Human intelligence is and will continue to be necessary to enhance the amount of data available to businesses.”

toIn the early 2000s, the first real data scientists were mathematicians, physicists, and statisticians. Working in highly specific departments, these specialists manually code and extract data to generate business insights. Data science remains an especially new discipline and building a curriculum from scratch takes time, resources, and instructors.

This gap between the current technology landscape and the skills being taught is an ongoing problem. We are living through one of the greatest periods of data creation in history, and companies are increasingly looking forward to it Utilize them to make quick and accurate decisions. However, the number of skilled professionals entering or returning to the workforce fails to meet the demand for business intelligence and related information.

As companies need to make more informed decisions, they must be able to rely on accurate data, and humans remain the most effective way to build those foundations. It’s about putting the right tools in the hands of the right employees—those who not only have business knowledge, but also understand the context of the problem at hand.

General misunderstanding

Perhaps it is because of this early insight into a new field that the data scientist has become somewhat synonymous with “data analytics,” as a result of a lack of understanding in the corporate world and among teaching professionals. The result is a general misunderstanding, suggesting that the solution to these data analytics challenges is to hire highly skilled data scientists who can craft handy software solutions to increase business value. In fact, learning to code alone will not be able to fill this data analysis skills gap. The solution to this STEM skills gap lies not with programmers, but with experts in other fields who are curious and data literate.

“If you get lost, you won’t ask for directions from the data science team, but from a taxi”

If you get lost, you won’t ask for directions from the data science team, but from a taxi. This experience is irreplaceable. By the same token, not all learners need to know how to code. Python does not turn the average person into a high-level data analyst, so this computer language should not be a mandatory data analysis skill. Fair appel à des collaborators experts dans un domaine et les former à des outils d’analytique accessibles signifie qu’ils peuvent travailler plus étroitement avec l’equipe de science des données et, bien souvent, qu’ils peuvent résoudre leurs propres problématiques à leur Rhythm.

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