In her mid-forties, C.S. Jyothirmayee enrolled for an 11-month programme in business analytics and data science, and the newly-acquired skill has brought a new dimension to her work.
“I use a lot of R and Python to carry out programming,” says 46-year-old Jyothirmayee.
In case you thought this was just another case of a tech professional taking things a notch higher in her profession, you could not be more wrong.
Jyothirmayee is a biotech professional focussing on patenting processes. “I now use data science in patent analytics and genomic research,” says Jyothirmayee. This may be counter-intuitive, but artificial intelligence, data science and many other tech-related specialisations are today as much for non-tech professionals as they are for techies.
There are figures to buttress this argument.
In the last one year, Simplilearn has witnessed a 30% growth in the number of non-techies taking up technical programmes including Machine Learning, Data Science, Cloud, DevOps engineer, cybersecurity and full-stack programming.
At Great Learning, close to 45% of enrolled students come from B.Sc, BBA, MBA, B.A, B.Com backgrounds, and this signifies increased interest in these specialisations.
At Masai School, an institute offering coding-centric learning, almost 70% of the candidates are from non-technological software backgrounds.
Irrespective of the educational background, one needs to have good aptitude — that is, logical reasoning and grounding in basic mathematics, says a spokesperson from Masai School. Similarly, Coursera’s ‘AI for everyone’ is also a non-technical course where beginners get to learn how to build AI projects.
“AI is not so much about coding as it is about coming up with creative models to solve your business problem and it is possible to build AI tools without typing a line of code,” says Usha Rengaraju, a data scientist who is also an AI consultant for start-ups. In certain fields that are essentially not technology-heavy, AI is making its presence felt.
A medical project
Sankara Eye Hospital in Bengaluru is working on projects to use AI in screening diabetic retinopathy, age-related Macular Degeneration and Glaucoma. The doctors working on the project have not taken up any formal training in AI, but understand how its works.
“We use AI to help analyse the data (images) to make certain decisions, in this case it presents normal vs abnormal images and we help it identify the level of abnormality and urgency of referral,” says Dr. Divyansh Mishra, consultant Vitreo-Retina and Ocular Oncology, Sankara Eye Hospital. “We are running the algorithm at screening camps where a trained optometrist captures a retinal image using a low-cost solution and feeds data into the AI platform to help it diagnose, counsel and refer the patients to the base hospital for further treatment.”
Vikram Raju, working in the “non-development” side of IT industry, has currently enrolled for a couple of courses relating to AI and machine learning.
Raju did not wait for the nudge from his company to upskill, and he is doing these courses just to be prepared for the future.
“I am also leading a team and it’s important for me to show them what they have to do,” says Raju. As he comes from a non-development background, how easy has it been for him to learn these new technologies?
“If I were to compare myself with those with a tech background who are also studying at the institute, initially what took them 30 minutes to learn, would take me 90 minutes. Now, I have improved a lot and I can complete in 45 minutes, what would take them 30 minutes to do,” he says.
Finding jobs
How employable are these non-tech graduates?
Hiring mangers say it is not easy to hire someone on the basis of a certificate they provide.
“The way to be employable is to show how they are able to implement what they have learnt, practically,” says Vinay Trivedi, HR Head, ToneTag. “Don’t just look at adding these new-age technologies to your CV without knowing how to incorporate them in your work,” says Trivedi.
Certification should be followed up with a few efforts, say HR professionals, and these include building one’s resume by going for coding challenges and hackathons where they get to work on real-life business situations; working on independent projects or trying to get an experience to build something based on algorithms; or, working on pro-bono projects.
