Only if they are being hired by hiring managers with degrees in business who are new to the tech industry. If the hiring manager’s background is in CS or other engineering fields, or has been in the tech field for a while, they would not hire a Brown or Dartmouth graduate over a Purdue or UMD graduate.
Moreover, you are ignoring two basic facts of hiring in tech - internships and recruiting. Tech companies will prefer to recruit employees and interns from schools which either have a strong reputation for engineering and those with a large selection of students.
So will they come to try and recruit from Dartmouth, which has 227 declared engineering majors (juniors and seniors only), or from Purdue, with almost 9,000? Even if they are looking just as Purdie juniors, we are talking about some 1,600 students.
Many more companies come to Purdue engineering, looking for interns and employees, than come to Dartmouth engineering. All you have to do is to look over the engineering career webpages that Purdue has versus that of Dartmouth engineering. Moreover, are Purdue, different departments have their own career resources.
So Purdue has general resources for engineers, but also has career resources for Civil Engineers, for biomedical engineers, etc.
So no, neither Dartmouth nor Brown will provide better career opportunities than Purdue.
This is where I would be careful to caveat. For engineering that’s true, but Brown CS specifically was a bad one to pick here as they are actually quite known in the tech world. I’d personally take it over Purdue CS every time, but people tend to lump in the engineering reputation of Purdue with the CS program when they are quite distinct fields in practice.
Using their career data for CS majors only here, here’s some quick breakdown:
In 2019, postgrad employers breaks down as follows:
15% Google
10% Facebook
8% Microsoft
If you add those plus some other big tech names such as Apple, Amazon, Airbnb, Uber, Tesla, Twitter, Stripe, Slack, and Pinterest you get nearly 50% of the ndustry bound graduating class, and that’s leaving off many more known tech names like MongoDB, Palantir, Yelp, Squarespace etc and known financial/consulting companies like Goldman, Two Sigma, McKinsey, etc.
Maybe this additional point of nuance will convince people to stop trying to find a good CS ranking list
They also ignore employers like the Federal Government which may not pay as much or offer stock options, but are one of the best places for job security. The proliferation of startups, and the prevailing philosophy in the tech giants of “any more time at a company than 5 years means stagnation”, there are many people for whom job security is very important (not to mention a pension).
There are also some really top level jobs in the DOD.
I haven’t yet seen any statistics as to how many people from each college end up working as engineers for federal, state, and local governments.
You find all those in the top 10 for Cal State Northridge, along with JPL and Naval Air Command, but no one is banging the gong that they should be in this discussion.
I do realize this comment is poking fun, but I actually think it’s worth noting a few interesting things in earnest:
I suspect the total of those is welllll under 33% of the class for Cal State Northridge. This actually highlights the limits of “top employers” as a concept given how spread out people tend to be in terms of companies to work at. When trying to extract meaningful data from Linkedin, I tend to go and pick a company of interest and use a granular people search to get a specific position, graduates from school X, and current/past working at company Y. Still, even then you need to check if its undergrad or grad degree, and intern or full time even. I have a separate rant I’ll spare this thread from about how Linkedin as a product has limited their search features. The point being, extracting meaningful data from Linkedin is possible but takes more time and nuance than using an overview Linkedin feature. Multiple “reports” have done exactly this over the past 5 years or so, but interestingly none seem to do it regularly year to year. I’d be curious if it’s a lack of interest or value or profit, but it sure does tempt using it for real careers data given how used it is. Linkedin is quite litigious about scraping though.
If I am actually wrong on Northridge and say 20% of the graduating class actually goes to those companies, that would really squarely highlight how little CS education quality/content varies between many schools beyond some top-level standouts and those without sufficient course offerings or too few professors. If true (again I don’t know but I doubt), maybe they should be banging that gong then
The “data” depends on so many things, not the least of which how many people a program graduates. My son’s school (Cal Poly) for example has more CS/SE students than all flavors of engineering/CS combined at Brown or Dartmouth. In general though engineering/CS is pretty egalitarian. That’s really the point I was making. It’s hard to rank something that is widely perceived as all mostly good enough.
Yes, but there are huge variations in CS rigor across schools (as well as class sizes, undergrad teaching focus, research opportunities, and formal incorporation of project work in to the curriculum).
That is to say, while anyone could get in to a Big N job (or regular software job) from anywhere (some more selective opportunities at smaller firms may only be at a few schools, though), there are major differences in the minimum expected and opportunities. Doesn’t mean someone couldn’t go far after going to Podunk U where the an OS final features a question like “what is caching?” (I’ve actually seen a test like that), but they’d have to develop themselves a lot more outside of the formal curriculum.
It’s more likely though that those opportunities will come based on a work resume rather than the school. Those types of jobs don’t tend to go to new grads no matter the school.
Yep. The LinkedIn data is pretty unrefined and self-reported (and smushes grad and undergrad together) but while the number of Brown CS alums on LinkedIn is a bit more than the number of Northridge CS alums on LinkedIn, the respective numbers who worked/work at Google/MSFT/AMZN/FB is 7 times bigger for Brown vs Northridge.
Many companies have pet schools and for some, I guarantee Brown is not on the list. That’s nothing against Brown. It’s a statement about the confirmation bias of having pet schools.
Tech companies are not only hiring engineers, and for various business legal, or other positions they will prefer colleges which are known for producing people who are well trained in these fields, even if the school’s engineering program is not a top one.
Maybe still true but not by several factors these days because the popularity of CS has exploded everywhere. Brown graduates almost 200 CS majors a year these days.
Many hiring managers are pretty non-technical. That’s just reality.
The proprietary trading companies and hedge funds (which are generally the highest paying CS opportunities for new grads, oftentimes paying 200-300k+ total comp for new grads) DO seem to recruit from Brown/Dartmouth more than UMD/Purdue, particularly when you look at proportions. That’s why I said being from Brown/Dartmouth MIGHT have greater access to opportunities. BTW the hiring managers at these shops are typically very technical.
EDIT: I am not saying their approach is right or wrong, but that’s their approach. They typically have a limited selection of “target schools”, and Brown and Dartmouth show up more consistently than Maryland and Purdue.
Feel free to check it out on LinkedIn yourself. Even better, some of the firms I mention below will release their recruiting schedules in the coming months on which schools they are visiting and when. Check that out too.
Companies to look at: Citadel (both LLC and Securities), Hudson River Trading, Jump Trading, Jane Street, Akuna Capital, Optiver, Tower Research Capital, Two Sigma, D.E. Shaw
Titles to search or add to your filter: Software Engineer, Research Engineer, Quantitative Researcher, Quantitative Trader, Quantitative Developer, Algorithmic Trader, Machine Learning Engineer, Trading Engineer, etc.
Note: Some of those titles are more appropriate to compare at the undergrad vs. undergrad level, while others are more appropriate to compare at the grad vs. grad level.
The point being, extracting meaningful data from Linkedin is possible but takes more time and nuance than using an overview Linkedin feature. Multiple “reports” have done exactly this over the past 5 years or so, but interestingly none seem to do it regularly year to year. I’d be curious if it’s a lack of interest or value or profit, but it sure does tempt using it for real careers data given how used it is. 
