Machine learning, AI, and data science are buzzwords we’ve been hearing for quite some time now. For good reasons, they’re the hottest career in the industry right now. Even since the pandemic struck the world, there have been furloughs and job losses.
The good news is, there’s still strong demand for AI, machine learning, and data science skills even during the crisis. Thus, if you’re inclined to become an AI engineer or a data science professional this may be the right time to upgrade skills and take your career to a new level.
Over the past decade, terms such as machine learning, data science, or big data have risen in the forefront making people confused. Regardless of their usage, there is a difference between all these terms. But our focus will be on two major job titles or job roles “AI engineer and data scientist.”
Both these terms denote technically skilled professionals whose focus is to majorly build, test, and deploy models while a data scientist’s job is to make complete sense of the data and derive actionable insights out of it.
Let us further define the differences.
Ideally, a data scientist is someone who fetches information from different sources. They come up with certain calculations and predictions about how the data can be of use to the organization in terms of business.
Read More: AI Engineers and Data Scientists: Is there a Difference in the Job Role?