In the recently published Forbes Tech Council article, “Just when A Data Scientist Has It All Figured Out, The Rules Change,” I had the opportunity to discuss the evolution of Artificial Intelligence and the future prospects for rising data scientists.
In the article, I presented some statistics from The Bureau of Labor Statistics that reinforces the growing need for data scientists. According to the research, the number of computer and information research science jobs will increase by over 22% through 2030. While this comes as great news for anyone looking for a job in data science, it also sets up a challenge for companies looking to fill much-needed AI expertise.
Not only has there been a scarcity of data scientists, but the new generation will be required to come with an entirely new skill-set. In the Forbes article, I go over some strategies, all centered around education, that should be considered and implemented– by future data scientists everywhere.
The new requirements can partly be attributed to COVID-19, which pinpointed certain structures that weren’t working anymore. Humans had to step in with what distinguishes us from machines- logical reasoning. Data scientists are problem solvers, but now more than ever, they have to be able to step out of the box. How can they do this? With continuous learning. Here are some strategies that will help promote that:
1. Professors must be accountable. Professors, once reaching tenure status, should be required to undergo continuous learning, not only in the classroom, but also by getting out in the field.
2. Data science certification should be a requisite. Just like in other fields, it is the professional’s responsibility to comply with certain regulations to remain licensed. Data scientists should be required to log a certain number of hours of professional development to earn and keep a “licensed” data scientist label.
3. Private business partnerships should be established. Universities should form stronger partnerships with private businesses with the aim to set up student apprenticeships. This would familiarize rising data scientists with the business environment.
4. Diversity must be required learning. Without the study of diversity and tolerance, data bias can ruin AI projects.
5. Data science should begin well before college. Data literacy should be a part of children’s curriculae to prepare them for the data-driven world.
Data will continue to drive economic growth and business decisions. To ensure that the future of data falls into the hands of a capable data scientist, academia must update its agenda and prepare future professionals for a new business landscape.
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