I am a triple postgraduate (PG) – master from Calcutta University in Commerce, an MBA in Finance from Institute of Management Education, Delhi NCR, and finally a PG in Data Science specializing in Finance and Risk Analytics from the University of Texas, Austin through Great Learning. A gold medalist & national scholarship awardee both in graduation and post-graduation, I have close to two decades of professional experience in financial data analysis and investment research. I have been the head of sell-side & buy-side investment research at Royal Bank of Scotland and Societe Generale, respectively, at their Fintech and global delivery centers in Mumbai and Bangalore, leading a team of thirty highly qualified and experienced professionals.
I started my career at GE Capital and moved to Moody’s Analytics and Sutherland before assuming the head of research roles. Currently, I am head of data & research at alphastream.ai, a Fintech start-up that specializes in financial research, automation, and transformation leveraging artificial intelligence and machine learning.
My challenge was to combine analytics with domain expertise, especially in areas like sustainability finance, an area that is emerging very fast. I also needed the right background, tools, and techniques in order to merge investment research with data analytics. Visualization has now become a key differentiator, and long reports are not always appealing to end-users. To handle immense amounts of data and text and do a meaningful sentiment analysis was something that excited me. Automation and Transformation were my highest priorities.
Online mode extends the flexibility, and it is always possible to repeat as well as it ensures you don’t miss a class as you have the recorded version also. There are many online courses, but this one had the hands-on flavor, and I was not interested in something like ‘AI and ML for leaders’ as I wanted to be close to execution levels.
This course, as I did my own research, had a project after every module that’s mentored and evaluated, and so that’s like 1 year of industry experience also we are getting with this course. This makes GL unique, which just does not restrict to a few recorded videos. It gives us a feeling of a batch we are studying with a feeling of university, college, or a classroom of peers with common objectives and interests.
Mentor sessions are interactive and complement very well with recorded lectures which are always one-way. In other words, the repetitive aspects are automated through video lectures, and interactive aspects are kept alive through mentor sessions.
The quality mentors chosen by GL make the journey very interesting and keep the inquisitiveness alive. All mentors have a great background and, most importantly, have diverse experience and qualifications. So that’s truly an industry expert pool with who we get to interact. They give us a different perspective and broaden our horizons beyond books and videos.
Words will fall short to thank everyone. I think the credit goes to GL management to choose them carefully and position them intelligently. They prepare us to become both a mentor or an academician as well as data science industry professionals. With any data set, I can now write a story, and I can go deep into model building and also effectively predict outcomes using python and statistical techniques. I am now able to do things differently in my own domain, where I have more than 20 years of experience.
Data Science & Analytics is one of the most emerging areas in career development. Earlier, you had childhood friends, college friends, apartment friends, pen friends, and also social media friends. Now you need to have data science friends. Please remember GL gives you knowledge, skills, and also a certificate but the most valuable thing that it gives you is a community. As an aspiring data scientist professional – please immerse yourself into the world of data, analytics, python, statistics, mathematics, and business report writing – also known as storytelling skill. Improve your visualization techniques – because ‘a picture is worth a thousand words. Please remember that every data set can tell a story, and every data scientist can write one.
