In this special guest feature, PK Agarwal, Regional Dean and CEO at Northeastern University-Silicon Valley, discusses the need to train new data scientists, and the value of continuing education for those already employed in the field. Agarwal, an accomplished executive whose distinguished career in high tech spans the public, private, and nonprofit sectors. He holds a bachelor’s degree in mechanical engineering from the Indian Institute of Technology in New Delhi, and two master’s degrees—one in mechanical engineering from California State University, Sacramento, the other in operations research from the University of California, Berkeley.
Every day we are creating more and more data. From tweets to crime statistics to health care records, all of it adds up to an incredible amount of information for someone to dive into, sort and figure out the next step. Thousands of the very people who do just that descended on the San Jose Convention Center in late March to learn more about the future of the profession. Many of them shared an uneasy feeling of uncertainty about the future of this fast-moving field.
Northeastern University-Silicon Valley conducted a survey in the heart of Silicon Valley at STRATA, the largest annual gathering of statisticians. The results showed that a huge majority of those in attendance (96%) thought that acquiring new skills in data analytics would help them with career opportunities and growth. Even more interesting was the fact that more than two-thirds of that group (68%) were already employed in the data science field. In other words, even those people considered to be at the top of this field don’t feel they are aptly prepared. Nearly two-thirds of respondents (63%) ranked data science-advanced analytics as their most coveted skill to learn.
It became clear that there is a near constant need for up-skilling in this rapidly transforming territory. A model candidate has to first have the skills to manage the data. They then have to be able to extract what is crucial and decide what to do with it. Top level employers know this all too well. Their ideal candidate has the combination of both of those traits, data management and data analysis. Survey respondents felt their skills were lacking in the areas of advanced analytics, data mining, Hadoop and statistical computing.
Another survey finding was surrounding the preferred method of obtaining these skills. For many of these professionals, returning to school full-time simply is not an option. Consider the constraints of professional obligations, as well as personal commitments, and it becomes clear that the most convenient option is online learning.
This method does have its limits. Researchers who developed programs for Northeastern University-Silicon Valley found that students need some degree of interactions that come with having a professor, fellow classmates and hands-on learning. This hybrid education strategy combines flexible course offerings with unique approaches to job training and mentoring programs. Northeastern works with 3,000 corporate partners to better match curricula and training programs to high-demand STEM jobs.
Data science will continue to evolve at a rapid pace and its workers have no choice but to keep up. The future of everything from companies understanding of how they work, medical breakthroughs and criminal justice will all be transformed through a deep understanding of data. That’s why it’s significant that a portion of our current data scientists don’t feel adequately prepared for the future.
For data scientists to stay ahead of the curve in this fast-evolving field, they must continually focus on upskilling themselves in the best ways that suit their learning styles while providing them with the skills to succeed. The best way to do that is through a combination of online courses, live classroom lectures, collaborative team projects and experiential job trainings.
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