Hardest Class vs. Favorite Class

mica-2012 Viterbi Class Leave a Comment

Senior year has come and I’ve gone through almost 90% of all the classes required of an Industrial and Systems Engineering major. How time flies! Now that I look back at all the courses I’ve taken and professors I’ve met, I can say that what my hardest class and my favorite class have in common are that by far, they are the classes I got the most out of… For different reasons, of course.

The hardest class I’ve come across is ISE-220, Probability Concepts in Engineering. Let’s just say I wasn’t a “natural” when it came to probability. It was a tough course because none of the problems we encountered had that single approach that solved it all. There are many ways to tackle a problem, and it was challenging for me to find the right angle to visualize the problem and draft a solution. The logic from the course was presented with a lot of structure by my professor, Prof. Patel, so that helped equip me with the right tools and techniques. My take-away from this class is, it’s crucial that I visualize a problem based on the logic presented by the professor, but also in a way that makes sense to me based on my previous experience and my academic strengths. In engineering, none of the techniques we learn is a one-size-fits-all problem solver, so we have to be pretty flexible in using mathematical models and frameworks we’ve learned in other classes to help us deal with the problem in front of us.

My favorite class would have to be ISE-330, Introduction to Operations Research and Deterministic Models… also known as Optimization! Is there anyone out there who can’t benefit from a little optimization?? The frameworks we learned in class — from the Simplex Method to solving the Transportation Problem — are all handy tools that I’m glad I have in my back pocket. I learned how to optimize decisions, minimize costs, choose the most efficient route, and find the best possible solution to the most typical problems industrial engineers come across. All these techniques (like the Simplex Method) are quantitative models, which are very objective. However, when I formulate a solution to a problem in class, I have to be aware of the subjectivity involved in making a business decision. So in class, my professor, Prof. Steve Stoyan, made it a point to balance the way we solve optimization problems, taking great care not to treat it like a purely objective mathematical equation; rather, as a complex problem that must take alternatives and special cases into consideration. It was a great experience taking the course with Professor Stoyan, who used many examples from his industry experience.

Want to learn more? Here's the best place to ask: