I, as a child, passionately loved solving Mathematics puzzles. I left behind the opportunities to join IITs but chose to study Mathematics and Theoretical Computer Science at Chennai Mathematical Institute. After completing BSc., I was not sure what to do and wasn't necessarily ready to follow the trend and do a Ph.D. like my peers did. I was more curious to see real-life applications of what I learned. I loved Game Theory and wanted to explore its use cases, which made me pursue MSc. in Quantitative Economics from ISI Kolkata. I received an offer to join McKinsey, one of the world's best management consulting firm as an Analyst when I didn't even know what Analytics is. McKinsey introduced me to the world of Analytics and Data Science. And I finally found the answer to my long search - where to use what I learned so far.
Students interested to pursue Data Science at their undergraduate can take Statistics, Computing or Economics at their 11th/ 12th standard
Today's young generation is way more smart and aware about opportunities available around them. Due to huge industry demand, they know that Analytics is one of the highly demanded skill now and therefore one of the best-compensated career options. But what they don't know is where to start to become a Data Scientist? Most of them prefer to go for Engineering courses and pursuing any field of engineering irrespective of their true choices. They land up into a job which not necessarily their choice too and very soon become frustrated. Because they have heard about Analytics, they want to now change their career path towards Analytics and opt for many online or executive courses available. I call these courses as 'capsule course'. It's like taking an aspirin when you get a headache.
It might reduce a headache for the time being but may not solve the actual cause of a headache. From these courses, candidates get a perception that they can finish these short 6-9 months crash course and become a data scientist quickly. But learning a couple of coding tools or few analytical techniques doesn't make one data scientist, because they don't get a holistic picture of what Analytics or data science is. They get a very restricted picture and come out with colored glasses on their eyes and judge the opportunities from those lenses too. It either creates another round of job frustration or their growth after a couple of years become stagnant.
When I ask these people why they want to join Analytics field, they mostly say 'because my current career path has no better growth' or 'because Data Science career pays better'. I hardly hear 'because that's where my passion lies'. It's a huge system failure, therefore. But it's not the fault of the candidates. Because we don't have a suitable course which can structurally introduce them to the world of Analytics or Data Science! They don't get time to love the subject or build their passion into it or explore the subjects in details.
Scopes of Data Science
First of all, we need to understand that Analytics and Data Science is not a pure science, it's an amalgamation of multiple subjects, with key subjects being Statistics, Computer Science and Econometrics. Other combinations also work, like Biostatistics, psychology +Statistics, Applied mathematics, geophysics, oceanography, molecular biology, genetics, physics, neuroscience, nuclear physics etc. Analytics is nothing but taking decisions based on data.
China has already started introducing data science in their undergraduate courses and artificial intelligence and robotics in Masters courses because they know future lies there and if they don't have right talent pull ready to participate in this fast-growing Industry demand, then their economy will be at losing in the global competition. Why are we sleeping through in India? So, the time has come when we should introduce a full-fledged course at undergraduate level on Data Science at most Indian universities. Its preparation can be started from the secondary school itself.
Students interested to pursue Data Science at their undergraduate can take Statistics, Computing or Economics at their 11th/ 12th standard. The undergraduate curriculum can be prepared in connection with expert practitioners. Curriculum needs to be revised often too along with new innovations and market demand. Roughly - the first year of UG, students should study the basics of Statistics, Economics and Computer Sc. In the 2nd year, they should study advanced topics of Econometrics, Predictive and prescriptive modeling and Big Data architecture and how to work on them. In the first 6 months of the Final year, they should study at-least 3-4 optional subjects which will give them the applications of what they have learned.
When this becomes a complete 3 years undergraduate course, students will learn the topics in detail, their foundation will be strong, learning will be holistic and for corporate organizations, it will be easy to hire, their hiring cost and risk will be minimized. If the curriculum is designed along with practitioners, students not only learn the theory but also, its applications and thus employability of those candidates improve. It will be a win-win situation for both the academic and corporate too.
Having over 10.5 years of rich experience in Big Data Analytics - Historical, Predictive, Contextual & Cognitive - and their detailed applications in multiple facets of Marketing spread across industries - Telecommunication, E-commerce, Retail Banking and CPG, to name few, Ujjyaini Mitra is currently handling the position of Head Analytics & AVP Strategy at Viacom18. She is also passionately working towards voluntary Social Entrepreneurship - in the Data science domain, to bring the fruit of data science to the unconventional sectors like Agriculture, Sports, sanitation, women empowerment, child abuse and so on.