Interested question posted on Quora recently. Here is my take on this.
Just put the next buzz word on your resume when you graduate, maybe AI engineer? I completed my PhD in computational statistics 25 years ago. It was in fact data science (image remote sensing), but under a different name. I changed my job title from statistician to data scientist many years ago, and I may dot it again if needed. There is more and more data to process, so the need will grow, but it will grow very nicely especially for those who can easily automate the mundane tasks, using tools available today. If all you learn at school is what anyone can learn by buying a book on Amazon for $40 (R, Python, Machine Learning), then you need to somehow acquire competences that are more valuable in the corporate world as “free knowledge” — stuff that anyone can self-learn — tends to be outsourced to foreign countries as soon as it makes sense, leaving those who paid $40K for their education, with more limited prospects. This advice applies to any field, not just data science.
Being highly creative could help you start your own company after graduation, as there is still so much poor data science technology that needs substantial improvements, be it fake news detection or search technology. And many fields (healthcare, recruiting, law, education, politics, sensor data / IoT, security) that are just burgeoning with real data science needs. But you need to have a strong business acumen to succeed, if you want to market and sell your solutions (or else, find the right partner to work with you.) One day, traditional data scientists will be viewed the same way MBA’s or even lawyers and doctors are viewed today: a dime a dozen. That is, you will need to find something to differentiate yourself from the crowd.
There are also plenty of systems that can be arbitraged nowadays. The first that comes to my mind is stock trading if your data science techniques — if you can design original ones — work. Advertising is another one. And these systems do not even require you to deal with clients. But again, you need to come with a mouse trap that no one has thought about before. And you need to gain domain expertise to be able to compete with other experts (you could start learning this experience via an internship, or via working for a company when or before earning your degree, or via self-learning and experimentation.) Anything learned in school or in a book, is of course something that millions of people are familiar with, resulting in strong competition from other people who learned the same thing.
Finally, most of the applications of data science haven’t been uncovered yet. There is a lot of potential there, either as an inventor of such new applications, or as an employee. You just have to be at the right place at the right time. Doing some research, constantly, to discover these opportunities, will help more than waiting for a recruiter to knock at your door. An example, among many others, is replacing dying bees by drone bees to help with pollination. It could have big impacts for the survival of human beings and other species on this planet, and the algorithms that those little drones would have encoded in their chips (the artificial brain), would be powered by data science — machine learning, more precisely.
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