Why doctors, fashion designers are learning data analysis, AI
As technology becomes critical for careers and businesses, many outside the field of technology are trying to acquire digital skills of the new era.
Anandita Bhuyan, twenty-two, leaves the philosophy of MBBS and BA. I knew I was ready for fashion and wanted to apply technology. The northeast plunged deeply machine learning (ML), attended technology forums and taught herself nuances of ML. She used web scraping technologies to collect large amounts of fashion data from the web. Then he applied technologies such as image recognition in this data to classify them into different categories.
“Our ML models recognize design details, colors, patterns, products, personalization trends, sustainability trends, human feelings. And we use them to provide information to fashion brands, says Anandita, who has a fashion analysis firm called Moodboard Analytics. What would have traditionally taken weeks and months to do in Excel sheets, Anandita can do it in minutes.
As technology becomes critical for careers and businesses, many outside the field of technology are trying to acquire digital skills of the new era, turning the conventional education system into their head. Almost all of the training companies that TOI spoke with said the number of non-computer students attending their Data science and the analysis programs are growing. At Jigsaw Academy, 17-20% of the students are not students of computer science, at Simplilearn, it is 30%.
Great Learning co-founder Hari Krishnan Nair says 50% of students for their Data science -related courses are from a non-technical background, up from 10% when they launched the courses. “In the last 12-18 months, we are seeing demand from chartered accountants, doctors,” he says.
During the course, says Nair, some learn to code more than others, in languages like Piton , and then begin to specialise in AI and ML. It’s a natural progression of Data science .
Kartik Shinde, a partner at the consulting and auditing firm Ernst&Young, says that when the audit began to be digitized almost 15 years ago, one could see public accountants acquiring technological skills. I don't think that college or any other educational training really matters when it comes to technological skills. People would still do well because, for them, moving to a new area is guided by the survival instinct, ”he says.
Kiruthiga Babu, a student, says that while pursuing her Masters in molecular life science in Germany, she took a shine to Data science and decided to change tracks from a purely research-based role. “It was initially tough, but having spent a couple of months on basic coding, I am now more inclined to take up a course on machine learning and pursue a coding-related career,” she says.
Dr Vikas V, a neurosurgeon at National Institute of Mental Health and Neuro Sciences (Nimhans) in Bengaluru, is pursuing a PhD in surgical robotics from IIIT-Bangalore. He’s trying to build a robot that can do surgeries. “I learnt coding way back in 1991. I have tried coding on Genesis, a computational neurobiology platform, and have taken Udacity (online) classes. I know some amount of Piton ,” says the doctor. He has already pursued several projects in ML and Data science in the areas of pathology and diagnosis.
Anand Narayanan, chief product officer at Simplilearn, says amongst its non-techie user base, the most popular programmes are ML, Data science , cloud, DevOps, cybersecurity, and full-stack programming.
Non-computer students can be classified in two in general terms, says Mayank Kumar, co-founder of the Upgrad learning firm. “Students are at the entry level or at the middle level. Once you have entered a few years in a particular industry, you would not opt for a completely new sector. On the contrary, he would try to learn a new technology that would amplify his possibilities in the current position. It is much easier for those in 0-3 years to make a total change, ”he says.
Divyam Goel, co-founder of the edtech firm AttainU, says that if the course is designed to help students understand the fundamental concepts of computer science, which are independent of language and framework, then it is much easier to learn any of the languages or frames.