@merc81 I wouldn’t use the Princeton Review as the best measure to make determinations about really anything. Also, that’s how it is at most schools with good STEM programs. Most kids start off with Multivariable Calculus or Differential Equations first semester, actually.
In terms of the 38%, I didn’t know that - good fact. In terms of the academic building, why does it matter how that looks? Most engineering schools have dozens of buildings dedicated specifically for math or science, which contain massive labs and million dollar equipment. Hamilton can’t compare in that regard, especially considering most top engineering schools (the institutions themselves) do between $100 million - $250 million a year in research.
In terms of rigor, I didn’t say it wasn’t rigorous. You must acknowledge though that it’s not the same. Again, I would refrain from using rankings from Princeton Review - they really don’t mean much. It’s not the same both in terms of the curriculum and how much is covered in a short period of time and in terms of application. I gave the clear example for Calc II, a lot more is covered if you go to an engineering school. In terms of data structures, it seems that it’s a lot less condensed at Hamilton and I don’t see a lot of topics there that I’ve seen covered in similar courses at other schools. The level to which the concepts are applied may also vary. At RPI, data structures homework usually takes between 20-30 hours each week just for that course. My friend who has taken the same course at Carnegie as quipped about similarly spending most of his week doing homework for his CS courses.
There’s also a much smaller course offering at Hamilton… It seems that algorithms and operating systems are both considered upper level courses at Hamilton. In a traditional CS program, those would be sophomore/early junior classes and be counted as 2000 level prereqs. Going from that, the only advanced courses offered after that are Database Systems and AI. Most schools on top of AI would offer computation vision, machine learning engineering, and several levels of each of those courses for concentrations in the undergraduate curriculum. This is to name one thing, of course. At most schools that are larger with a greater focus on CS,etc. you’ll find dozens of upper level electives in a much greater variety of concentrations.
Even considering other majors, for example, like physics it doesn’t seem like much is offered beyond what would be the equivalents of what physics majors would take by their sophomore year at another institution. You also couldn’t take very in-depth courses in the subject (at least compared to other institutions)- it’s not like Hamilton offers several levels of quantum physics, any courses on fusion (something found at only engineering schools), etc.
Going back to math, at many schools that have a strong engineering program students will learn linear algebra in their engineering classes starting in first semester. The greatest transition though is application. In a science or math class, you learn theories and how to solve problems. In an engineering class, you learn how to use linear algebra to run a circuit analysis using a program you made. It’s very multidisciplinary and almost entirely application. Imagine only having taking electromagnetism and having to design embedded control systems using only that knowledge as a prerequisite? It can be difficult. Also, Hamilton seems to have a much greater focus in its math department on theory. At many engineering schools, it’s a lot more applied math - granted, that can be a deal breaker for many people.