India Poised To Grow Rapidly In Data Science Education: Paul Kim, Stanford University

The popularity of data science education has soared over the last few years. Subsequently,  field’s job prospects have also gone up, with companies worldwide looking to hire skilled professionals to drive business processes. The nonlinear growth of data science has posed significant challenges for universities developing data science courses and individuals looking to pursue it as a career.

For this week’s data science career series, Analytics India Magazine got in touch with Paul Kim, the Chief Technology Officer and Assistant Dean of the Graduate School of Education at Stanford University. 

Known as an education technology entrepreneur, Professor Kim leads initiatives involving the design of learning technologies, educational research, and community development. His work aims at promoting innovation and competition by constructing a programmable and open mobile internet — POMI.

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In an advisory capacity, Paul has played a role in Saudi Arabia’s national online education initiative, the national evaluation of Uruguay’s One Laptop Per Child project and Rwanda’s national ICT planning.

In this interview, he provides an overview of the data science market and the challenges universities face in developing a practical data science course. He also spoke about the future of the aspiring data scientist for the current era.

Challenges with data science education market in India

All sectors have welcomed data science with open arms. The COVID pandemic has accelerated ICT adoption in teaching, learning, assessment, and administrative functions. “I would say COVID has created R&D opportunities along with available funding more than any other catalysts in the past few decades,” said Paul.

Professor Kim believes India is in a much better position in terms of the data science education market because of the multiple technology innovation powerhouses strategically located in India and their growing needs for the future workforce in data science and artificial intelligence. He said, “while big contenders are obviously the US and China, but with institutional on financing and governmental support in terms of policies and regulatory issues, India is poised to grow rapidly in the overall data science education and application areas.”

This has led many universities, edtech platforms, tech companies, and governments to come up with free courses during the pandemic. However, there has been a massive learning gap between the course structures and the skills required to land a job.

Paul said, considering data science is a rapidly advancing field, universities steeped in traditional models of governance and decision-making processes will have a hard time instrumenting data science courses. That’s why “universities in India must transform to align with many of competing alternative education options such as online boot camps and non-traditional talent development organisations.”

Paul also mentioned the importance of government and corporation involvement in encouraging more students to choose data science subjects. “Governments can figure out ways to remove policy and regulation related barriers while corporations can work closely with educational entities that are nimble and flexible to provide the most invigorating and fast-developing data science curriculum in the world,” he said.

Advice for aspiring data scientists

Paul stated, being well-rounded, skilled talents who can use a wide lens of viewing capability to understand the true needs of the industry and users while genuinely developing empathy to solve most intractable problems is the key to become a real data scientist. 

“Do not follow people around you, but develop your own unique skill sets, so you are rare species in the data science ecosystem,” advised Paul. “If you follow others and be just another data science worker, you may not be necessarily a highly sought talent in the whole ecosystem.”

While there are many online courses and MOOCs currently available for data science enthusiasts, Paul bets high on a professional degree in data science. A professional degree is for those who couldn’t demonstrate his or her talent with competitive problem-solving skills, said Paul. 

“Though these degrees can help get one to an interview if one cannot demonstrate their competency, they are not going to secure a career opportunity they want,” he added. 

“At the end of the day, what makes a difference between a competent contender versus a mediocre contender is in the genuine passion for being the best,” he concluded.

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Sejuti Das
Sejuti currently works as Associate Editor at Analytics India Magazine (AIM). Reach out at sejuti.das@analyticsindiamag.com

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