Hey everyone! I am a current senior. I got accepted to UC Berkeley for Data Science major and Carnegie Mellon for the Information Systems program. I am not sure what I want to do in the future, but my goal is to get a job at Silicon Valley. I am also thinking of going to grad school, but I am not too sure… I want to choose what is the best for me (in terms of salary) so please help me decide which college I should go to.
What are the cost differences?
UCB tuition costs about $40,000, CMU costs about $60,000. But honestly, I am not too worried about the tuition, I just want to choose the school where I can thrive!
Data Science == elements of (math/stats) + elements of cs. Both schools are excellent.
Are there any requirements at UCB to be able to declare the major? (like CS in L&S?)
Costs of tuition alone or did you also include housing/meals/transportation etc…?
Here is a link to declare the Data Science major at UCB. Declaring The Major | CDSS at UC Berkeley
Are you a CA resident or OOS for UCB?
Yes (in fact, a lot of classes overlap with CS)! Here are the major requirements: Data C8, Math 1A, Math 1B, Math 54, Compsci 61A, and Compsci 61B.
I have been going around asking for advice but the responses are pretty much 50/50. Since both schools are prestigious and well-known for STEM, it is difficult for me to choose where to attend. The only real difference I am concerned about is that Berkeley is solely focused on data science, whereas the Information Systems program at CMU combines data science with computer science, information technology, business, humanities, and social sciences, making it much more flexible.
Costs of tuition alone. I am an international applicant so OOS for UCB 
So costs with all included would be around $65K+/year for UCB. No bad option between the 2 schools as long as both are affordable.
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Agreed … so has to be non-academic factors then!
What do you prefer?
Bay Area or Pittsburg?
What’s easier to get to where you’re from?
Considering the location, would it be better to attend UC Berkeley since it is closer to Silicon Valley? UC Berkeley feeds more people to SV compared to CMU (basically no difference), but at the same time, Berkeley is a large public school (which explains why more people get employed), whereas Carnegie has a significantly less undergraduate population.
Both sounds amazing, to be honest! I have never lived in the States before so I do not have much experience with either California or Pennsylvania. Personally, which school would you choose?
You should consider the fact that if you REALLY want to learn Data Science at a very high level, you’ll probably want to go to grad school. Most advanced position in Data Science are geared towards PhDs … so it’s not just about employment.
Yes, I am definitely considering attending grad school! However, it really depends on how I perform at my undergraduate college.
I would choose CMU if cost isn’t an issue. Popular classes are much larger at Cal and IS majors at CMU seem to place pretty well.
Is it because you prefer a private institution instead of a public college?
That’s part of it. CMU will devote more resources per capita to undergrads. Cal is good value for in-state CA kids and it has a top brand name and location, but CMU IS grads place well.
CMU has very detailed info by major:
https://www.cmu.edu/career/about-us/salaries-and-destinations/
One consideration, though, which is that it will be very tough for an International to get a work permit in the US just after undergrad. So if the Cal brand name is amazing in your home country, it may be worth going to Berkeley.
What major would you prefer to study?
Note that UCB data science is not just CS and statistics. You also choose an area of application to go with it: Requirements: Domain Emphases | CDSS at UC Berkeley
Here is UCB’s career survey (including by major):
https://career.berkeley.edu/survey/survey
These are both good schools. How about looking at other things…weather, location, etc.
CMU Information Systems major is highly employable but very different from a CS or DS major. It covers a lot of areas but not in great depth in any of them. Make sure you understand the differences.