According to a Tencent report on AI, by 2025, there will be a shortage of 7,50,000 data scientists. Conventional learning will not be able to scale in order to accommodate such a large number. While part of the problem is scarcity of teaching resources, the other is the difficulty of becoming a good data scientist — despite the number of courses that claim to turn an aspirant into a data scientist in a few months. Clearly, there is a need to accelerate serious learning in Data Science and AI, and make it available to a large number of aspiring learners. There is the added challenge of establishing talent and enabling employers and the talent to find one another.
Massive Open Online Courses (MOOCs) do not replace traditional learning environments very well. MOOC completion rates are below 2-3%. One alternative to university learning as well as to MOOCs appears to be community learning, where, with a minimum of help, people help one another to learn purposefully. Despite the meteoric growth of Internet connectivity and its profound impact on all forms of dispersed communities, community-based learning has lagged. Perhaps, it is because long-term, high-activity communities self-select for advanced learners who likely have little patience for a beginner’s pesky and repetitive need for help.
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There do not seem to exist large communities that take a structured approach to framing a community learning environment and provide a push-pull system to engage participants and to keep them moving towards specified goals. What is required is a push strategy through goal-oriented learning, and pull strategy through incentives such as employment and the opportunity to create public credentials, in order to create an effective and active community resource.
To move beginners to more advanced levels, we must provide a robust frameworks, and an action-oriented approach. Which leads us to the final point that, when one does something, one needs a strong feedback and a reward system in place.
So, how to address the needs of advanced learners? It is easier as motivation and engagement require that one should only stimulate curiosity and facilitate productive engagement. Additionally, when presented with a curious, curated, and interested bunch of beginners, they are more likely to want to help. A reward system can be put in place here and has the potential to yield promising results.
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The pull strategy, however, is a combination of offering large scale, short, high-velocity, hybrid courses with top faculty who are pioneering Data Science education and training. According to the India Skills Report 2021, most of the 45.9% of employable resources are expected to acquire new in-demand skills through certificate courses. Eventually, it is all about learning relevant skills and securing a good job, and hence, career fairs and exposure to employers through talks and employment presentations will allow students to approach companies with more ease and convenience. It is integral to make sure that these forums attend to all community levels of Data Science learners — from explorers to serious learners. It will also attract experts who seek to share their learnings with an engaged and enthusiastic audience, and are willing to keep up with the changing trends in the industry.
With demand for data scientists rising exponentially, this is an area where community learning is essential to provide scale,. With luck, we will see more such initiatives in other learning domains as well.
The writer is Founder and CEO, Univ.ai