The term "AI" is used so often nowadays that we have a basic understanding of what it means: a computer's ability to perform tasks such as visual perception, speech recognition, decision-making, and language translation. AI has progressed rapidly over the last few years, but it is still nowhere near matching the vast dimensions of human intelligence. Humans make quick use of all the data around them and can use what they have stored in their minds to make decisions. However, AI does not yet boast such abilities; instead, it is using huge chunks of data to clear its objectives. This ultimately means that AI might require huge chunks of data for doing something as simple as editing text.
Data science is much more than just simple machine learning. Data here may not have been obtained through a machine, and it may not even be for learning purposes. Put, data science tends to cover the whole spectrum of data processing as we know it. Data science is not just related to the statistical aspect of the process, but it feeds the process and derives benefits from it through data engineering. Data engineers and data scientists have a huge role to play in propelling AI forward.
A few bigwigs of our students hiring companies in 2018-'20 include