With artificial intelligence and machine learning techniques growing in sophistication levels and use cases, companies can’t find enough data scientists. “Multinational corporations and IT & knowledge process outsourcing organisations are constantly on the lookout for skilled data science talent to analyse structured and unstructured data, and deliver relevant, actionable insights from them,” says Hari Krishnan Nair, co-founder of training company Great Learning.

But how does one become a data scientist? Until a few years ago, it involved getting a degree or specialisation (like MSc or PhD) in fields such as computer science, mathematics, statistics or other equivalent subject areas, says Arunabha Choudhury, technical architect – data science at FourKites, a real-time supply chain visibility software provider. But in recent years, he says, there have been a lot of efforts around democratising AI and ML. “Most of the research work in data science is now open sourced. If anyone is passionate about analysing large volumes of structured or unstructured data, finding patterns, and driving business decisions, they can now become a data scientist. A professional certification or a degree is desirable, but may not be mandatory anymore,” Choudhury says.
 

It’s a profession that requires aspirants to not only be technically proficient in data science subjects, but also have an excellent understanding of the business they are building solutions for. Deepak Warrier, chief data scientist at BlackBuck, an online trucking logistics platform, says on the technical side, you need a bit of mathematical aptitude to be able to extract a business problem into a mathematical problem. And you need programming skills because you are ultimately going to build a solution that is integrated into the engineering stack. “At a very high level, it’s a mix of mathematical formulation and programming, and really working closely with the product and business teams to understand what it is that we want to deliver so that we can align your systems and models along those lines,” he says.

It’s also a very new profession, adds Warrier, and therefore it’s often not clear what is and isn’t technically possible. “When I started my career, the word data scientist did not even exist. The challenge is that it is an amalgamation of multiple disciplines, and you also need to have a very good understanding of the business – how the products get instrumented, how the pipelines are being built, etc,” he says.

 

So as a student, you can have a beautiful career in data science and become data scientist.

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Source: Times of India