AI, ML or Data Science? Which is the Best Path to Take?

AI, ML, or Data Science, which is best for you?

Artificial intelligence, machine learning, and data science are the most trending topics in the present era. All these terms fall under the same domain but they have their individual applications and meaning. For the tech companies, the application of these terms is given the top list priority. Even the job hirings for these domains is increasing at a high rate. It has increased 74% according to Linkedin's 2019 "Emerging Jobs" report. The salary offered to a data scientist and AI engineer is quite high which is above US$100k. The growing need for talents in such jobs has triggered a number of universities to provide degrees in those domains, many new courses, diplomas, full-time courses, private boot camps have emerged and also are still emerging. But a question arises about which domain's course should be taken up. Is it AI, ML, or data science? Is there any difference?

Answering this, yes there are differences but the difference is not a definite one. There is a very thin line of boundaries among the three which cannot be ignored. Many universities tend to ignore the boundary and provide degree programs based on the combination of the three. This makes students unclear about which field their focus is on. So, it is important to mark the differences and provide proper guidance to the students.

Where does the difference lie between the three domains?

Artificial intelligence refers to machines that have the potential to replicate the cognitive capabilities that are associated with the human mind. It is about replicating human minds' capabilities.

Machine learning is generally considered to be the potentiality of a computer program to learn or boost performance through examples rather than detailed programmed rules. Machine learning is one of the essential tools which data scientists use to examine and interpret data.

Data science refers to the accumulation of methods, tools, and practices of analyzing data to interpret and derive insights in order to support decision-making.

Both AI and data science use machine learning as key tools. In AI, ML tools are used in real-time to allow machines to execute their action. ML is the essential tool in the field of AI to develop intelligent agents. In the field of data science, ML is used as a data analysis tool to unlock patterns in data and to make predictions.

What should you learn?

After understanding three different fields of expertise, you need to think about what your goals are and which one you prefer. If you want to go for research work then preferably the field of data science is the one for you. If you want to become an engineer and want to create intelligence into software products then machine learning or more preferably AI is the best path to take.

Still, if you are not sure which path to choose you can start with data science because after all data is everything, data is the key to success in all the fields whether it is AI or ML. Strong expertise in processing, cleaning, analyzing, and visualizing data, along with statistical knowledge, will benefit you no matter what path you ultimately take.

But it is important to note that if you are just starting your career, try to build your domain knowledge before spending your time learning about data science or ML or AI. If you have adequate domain knowledge you will maturely understand the problem you are trying to interpret and also recognize how AI or ML or data science can play a role in solving it.