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What does it take to Build a Career in Data Science?

Written by Dr. Marya Wani (Director of Programs – School of Data Science), as published in the Education Times

According to Forbes magazine, data science is the highest paid field to get into. Analytics India Magazine claims that there were 50,000 analytics positions that remained unfilled in 2017 and this number is set to soar to 100,000 in 2018. McKinsey claims that by 2018, US would experience a shortage of 140,000 to 190,000 analytics professionals and about 1.5 million data savvy managers.

We can safely conclude from these numbers that there is a real shortage of analytics professionals throughout the globe and this gap does not seem to be closing anytime soon.

Most of the sci-fi movies that I watched as a child have turned into reality today, be it Terminator (Sofia), Total Recall (Driverless cars), or Hal 9000 (Siri).  At the core of these revolutionary technologies are Data and AI. So, when we speak about Data Science and AI, it is not just a career prospect, it is the reality of the future that we are walking into.

Why Data Science has become so important?

There are a number of factors that have contributed to the exponential progress in the area of Data Science. Access to large amounts of data (we produced 90% of all data in the last two years alone), computing power with powerful technology infrastructure to manage these so called ‘Big Data’, programming ease & efficiency with languages like R/Python becoming mainstream, high adoption rate of data intensive technologies and data driven decision making across industry verticals, have all contributed to the development of this field.

Preparing our next generation for AI and Data Science, is a multi-stakeholder initiative and will have to be dealt with diligence, planning and focus on quality.

Industry, academia and the government need to play their part efficiently to bring about this revolution that can place India at a competitive advantage in the Data Science and AI industry. We witnessed an ‘Infosys moment’ in the late 90s and I believe we are at the cusp of the data enabled job revolution, should we play it right this time around as well.

What does it take to build a career in Data Science and AI?

We need to break the Data Science careers into its several streams to begin with. These professional streams include data engineering, data analytics, AI and Machine Learning, scientific research in the field of data science and so on.

Prospective students who want to pursue a career in data science should possess traits like curiosity, problem solving, and creativity. Among the technical competencies data science professionals need to master are a strong hold on statistics and mathematics, programming skills, algorithms and applications, business domain knowledge and research aptitude.

If one feels passionate about data science, then one should pursue a course that offers quality education in the above skill-sets and an exposure to real world business problems in data science.

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