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Data science has gained significant traction in India, emerging as a rapidly growing field with immense potential. With a blend of technological advancements and a vast pool of talented professionals, India has become a prominent hub for data science. However, there is still a critical need for upskilling in this domain. Analytics India Magazine spoke to Shivam Dutta, CEO and Co-Founder at AlmaBetter on the challenges facing the data science industry, the key skills needed and how AlmaBetter is bridging the gap.
- What are some of the key challenges that the data science industry in India is currently facing?
To say that data science as an industry is rapidly evolving would be an understatement. If we look at the amount of data being created every day, which by the way, stands at 3.5 quintillion bytes, it seems unfathomable how this data is being processed. That said, we need to, as a country of entrepreneurs and businesses, derive insights from this data and leverage it to move innovation forward. Currently, the skill gap is insurmountable, more so because aspirants are not equipped with the right technical smarts required to excel in the field. Furthermore, companies are also facing a crunch, contributed by a lack of talent and the amount of time and resources spent on filling just one vacancy. This is a playing field that ed-tech players can shine in, helping serve both stratas of the business world.
- How do you see the industry evolve in the next few years, and what are the factors that will drive this growth?
If we compare the demand for data scientists 10 years ago versus now, we can see a stark difference. A decade ago, data science had just popped its head above the water. Today, companies risk becoming obsolete if they don’t have a data science and analytics strategy in place. This alone speaks volumes about how the data science industry is evolving. As I said, the amount of data being generated today is exponential. Players like OpenAI have introduced large language models such as ChatGPT, and several others are building products that have human-like intelligence. With the gradual increase in users who are leveraging automation and the general interest in automating error-prone manual tasks, it is safe to say that the hype around data science and AI is not temporary. This will not only help remove redundancy, but also move the economy towards the tailwind powered by AI.
- How have the hiring needs in DS evolved over the years?
The constant to and fro between the demand for experienced professionals and the lack of hiring trends for freshers has given rise to a Catch-22 situation. In my opinion, to counter this, the right approach would be for educators to rise to the occasion and provide training that not only prepares students in theory but also in practice. Long-form training programs that offer training equivalent to work experience and exposure to project-based learning can help mitigate this issue and solve the talent crunch. This is exactly what AlmaBetter’s Full Stack Data Science Course aims to do. It is necessary to break the vicious cycle of freshers not getting experience, and experienced professionals not getting the right opportunities.
- What are some of the key skills and qualifications necessary to succeed in the industry and how can aspiring data scientists acquire these skills?
The product life cycle in data science is extensive and requires deep understanding. Anyone aspiring to succeed in the field will have to dive into the deep end of the entire life cycle, starting from business acumen to core tech skills like data engineering and modelling. While they must be experts in understanding data modelling and transformation, choosing the right machine learning and deep learning models, and productionising those models, they must also be adept in understanding and converting a business challenge into a data science problem statement.
That said, communication and cross collaboration skills are also paramount, especially since it strengthens the output for projects. We identified this gap in the current educational approach and, hence, have created a holistic learning model at AlmaBetter, which offers intensive training on all three fronts.
- How is the education system in India preparing students for careers in DS, and what changes could be incorporated to better align with the needs of the industry?
If you look at the current offerings that colleges are putting forth, it is easy to identify a sore thumb sticking out, which is, UGC has its limitations. While colleges are adopting data science in their curriculum, they are unable to offer end-to-end training in the field. Data science has been reduced to a mere specialisation coupled with a traditional degree, with the education imparted being incomplete and not fit for real-world application. Limited training that captures only a fraction of the entire data science scope has further led to a lack of skilled data science talent. Unfortunately, these pseudo-data science degrees offer a fake sense of knowing these new-age technologies when in reality they gain expertise only at a minimal level. We, as educators and torch bearers of advanced education, must ensure that students are offered with the right material, exposure, and understanding of the full scope of data science. A change in the approach and curriculum is long pending.
- There are concerns that the rapid development of AI technologies may lead to mass job displacement, rendering some data science and AI skills obsolete. How does AlmaBetter address these concerns and prepare its students for an uncertain job market future?
The growing popularity of AI technologies has given rise to a lot of uncomfortable conversations. However, we need to better understand the scope it offers. To be honest, the only jobs at the risk of becoming irrelevant are those that are also at the risk of being inefficient. Several fields today, including research, content, and design are adapting to this new revolution and leveraging AI technologies to build robust efficiencies. As we become a technologically advanced civilization, knowing how to leverage new-age technologies, like ChatGPT and large language models, will give professionals a competitive advantage over their contemporaries. This is also being catered to within our Full Stack Data Science curriculum through prompt engineering, where we prepare students for such roles.
- What role can government policies and initiatives play in supporting the growth of the Data Science industry in India, and what steps should be taken to encourage innovation and entrepreneurship in this field?
Currently, while there is some government support, I wouldn’t term it as adequate. As more new technologies are entering the market, India as a developing country should have the policies and infrastructure to modify the curriculum and train students on relevant industry requirements. I also believe that establishing more incubator funding programs will give ed-tech players a platform to further the agenda of high-impact technical training and, subsequently, build a workforce that can take the country to new heights.
- What approach should companies take to identify, train, and groom data scientists?
As important as it is to train the workforce of the future, it is also vital that skilled talent is identified, groomed, and rewarded for their adeptness and abilities. In order to do that, there are a few strategies that companies can adopt. The first thing that comes to my mind, and can definitely become a stepping stone, is that organisations can roll out mini projects as externships to ed-tech players who have training capabilities in the domain.
This will not only help the talent of tomorrow to become deft in current industry demands but also help them gain much-needed practical experience. Companies can also explore options to offer corporate training for their existing employees, which will boost productivity and employee satisfaction, and contribute to bridging the skill gap. Thirdly, one of the most bothersome issues in talent acquisition has been vague job descriptions. Companies must be transparent with regard to what they expect from a particular role. Inflated titles need to be shown the door and clearer roles need to be chalked out.
- What advice do you have for individuals considering a career in Data Science in India?
To overcome any challenge, it’s essential to understand what causes them. My first and foremost advice would be for professionals and aspirants to do their research and identify the gaps and talent crunch. Having an astute understanding of this will help them leverage the skill gap to their advantage and carve out their careers where they are indispensable. Next comes preparing for the right roles; following an unstructured path often leads to blockers. Hence, it’s important to set proper goals and excel at time management. Lastly, staying abreast of new technologies and their trends can put professionals at the summit of the talent pool.
- What do you see as the most significant opportunities for the data science industry as a whole in the coming years?
Hiring in data science is going to see an upward trend in the coming years. Despite the current economic situation and layoffs being the talk of the town, data science and AI professionals have been thriving. This can be attributed to the constantly evolving technologies and the exponential demand for quick-witted and business-smart professionals who can derive stories worth telling and revenue worth earning from data. The country as a whole should get on the ride to experience some really cool things that data science and AI will bring forth.
