Why Operations Research?

Ever since I decided to “switch” from studying computer science in my undergraduate degree to operations research in my master's, this question has been posed by family, friends, classmates, literally everyone. I thought I’d share my answer to “Why Operations Research?” in a blog. Plus, it’s been YEARS since I wrote one anyway. Some have also asked “Why NOT a master’s in computer science?”, and I think that’s a discussion best suited for a separate blog!

I first came across the term “operations research” (OR) sometime during my 3rd-4th year of engineering. All I had were answers from Google. From my limited search result understanding, OR seemed like a very math heavy degree. So, I decided to do the next logical thing - ask my math professor about it. He is, by the way, one of the best professors I have been taught by. Dare I say the best professor in Manipal?

My professor said he had “textbook level basic knowledge”, and not any more than that. I reached out to more people on LinkedIn, friends, friends of friends, to try to find out everything I could about this field. By this time, I was also starting off my research journey in Natural Language Processing. But eh, I was merely just wandering.

Let’s talk a little bit about my understanding of OR, which of course, has consistently evolved over the past couple years. OR majorly revolves around decision making for complex systems, in simple words. It is analytically very heavy, as decision making usually is. Many big companies employ OR methodologies for sales, supply chain, marketing and other management related applications, which is why you’ll often hear the term “management science” used interchangeably with OR.

Lately, I have been getting very interested in the marketing and advertising applications of it. If you have had even a slight look at the “about” section on my blog, you’ll know my obsession with social, media, and entertainment applications for everything I learn.

Another major part of OR is optimization. Optimal decision making. Optimal algorithms. This is where I feel my computer science background happened to be kinda useful. We are very used to writing code using the best data structures and algorithms. So, I found it very interesting to see it being used in a wide array (😉) of applications. Next semester, I’m taking a class on Machine Learning Optimization, which I’m really looking forward to.

As several other articles describe it, OR is a beautiful combination of both art and science. While backed by scientific proofs and algorithms, applying OR at the right place and getting good use out of it is definitely an art.

Summing it up, I found all of this near-perfectly in line with my career goals. I believe that having a strong Computer Science (CS) foundation enables me to use OR to make CS applications better, and vice versa. An interdisciplinary combination of both is, I believe, huge for organizations who are able to apply it well.

Sooo, that’s why, OR :)

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