This blog started with me trying to figure out a way to share beautiful visualizations I created at the very starting of my career. And then eventually, I started posting other things as well.
Now this place is more of my diary containing a collection of my unpublished works (good that you can't see them) + unfinished projects + some ongoing projects (see RALKS) + a forum for people whom I believe in.
But the pattern remains the same, everything revolving around my curiosity towards data.
What people find odd about me is that I am strongly attracted towards 'Art' and I believe that combining Art with Science and Engineering has extremely high potential of impacting the society in a supercharged positive way. And I am addressing that in my own secret way. Watch out for some announcement by the end of 2019.
I have been working in Finance domain since last two years and have a good understanding of Software Engineering with Data Science. I am involved in working on some sophisticated financial portfolio modelling techniques for one of the world's biggest Investment Management companies.
Prior to this, I was working in the product team of a payment company Happay, laying out the product details in a more data driven fashion. This experience taught me how critical it is for a product to focus on the problem it is trying to solve, instead of everything else. It's simple but I tell you not easy. These guys are good.
I have also published research in some good international conferences, but discussing about that here would make me boring.
I am comfortable using Python, R and SQL and I have some experience with Tableau. I have good intuitive understand of different types of Visualizations, and have experience in designing products for the same, imparting information in the most impact-full and clutter-free way to executive teams.
I have used and continue to use math in my daily work. And I have studied even more math while completing my Electrical Engineering. I also have some experience in leveraging ambiguous statistical procedures to take out meaningful inferences, and understanding various fallacies while deriving those inferences - all from data.
I have always believed and continue to believe in the contributions of non CS people in Data Science, unlike how it is projected in my area. Because it is the most 'unlikely' people who make the most 'unlikely' advances. Achieving 'likely' advances are just a matter of time. And I am working on this issue.
You can reach me out at
I love you.