Imperial College London vs UC Berkeley

As of late March, I’ve received acceptances from UC Berkeley (my dream school since 8th grade but now it just seems meh cuz ive got it) and Imperial College London (and others). My Cal course is Data Science BA (doesnt matter as we start out as undeclared anyways) and my Imperial course is Computing MEng with a focus in AI and ML. My current dilemma is that everything (apart from a solid academic foundation) is better at Cal, but if I want to actually be industry ready then Imperial is the better choice (as it is a proper Engineering course).

There are two things currently in my mind:

  • Imperial vs Cal
  • If I do choose Imperial, and then decide to go to the USA for grad (my new dream is Stanford), how easy/ beneficial will that be to get me a position in Silicon Valley?

Any help is appreciated!

Is your end goal to work in the US or UK? Do you have a UK passport?

Is your interest moreso in Data Science or Computing. I hope you understand the differences between these two and their employment opportunities as well.

Sounds like Imperial is better for your academic and career plans. You should have no problem getting into a good US uni for postgrad as long as you do well.

How do the costs compare?

If all things were equal, I’d advise Cal if you wish to work in SV. However, all things are not equal. Data Science as a degree is newer and buzzier, and may not be perceived as much as a “real” degree. A MEng from Imperial carries a lot of weight and is a safer bet.

OP: Is Imperial College of London a 3 year degree program ?

If so, then consider earning a masters degree in data analysis or in data science at a US university during the fourth year.

Data Science & Data Analysis degree holders are in high demand.

Data scientists and data analysts are in high demand. From what I have seen, those jobs tend to be filled by advanced degree holders in mainstream STEM fields, not by undergrad data science degree holders.

But I work in STEM. Maybe it’s different in say marketing.