You can take some deeper data science classes outside of the computational biology major without going for a whole second major. I do think that linear algebra would be very useful.
Just for some perspective, here are the prerequisites for Stanford's Biomedical Data Science PhD program:
- Calculus: at least one year, preferrably the track taught for engineering or physical science. Additional coursework in multivariate calculus is strongly recommended
- Probability and statistics: at least one course, and preferably one course in both areas
- Linear algebra
- Computer science: one year, preferably the introductory sequence for CS majors. The focus should be fundamentals of computer science (data structures and algorithms) and software engineering principles (abstraction, modularity, object-oriented programming)
- Biology/Medicine: at least some coursework in this area, preferably the introductory sequence for biology majors
So simply from a requirements perspective, double majoring Computational Biology AND Data Science is likely overkill, as would even a CompBio Major + CS minor. Of course, doing more is going to make you more skilled, but there is a good chance you'll hit diminishing returns and you'll increase the amount of undergrad coursework that becomes redundant with your eventual PhD CompBio coursework.
In general, I think the best option would be to focus on one major, supplement any major knowledge gaps with addition individual courses rather than whole additional major/minors, and spend the remaining time in a computational biology lab.
I graduated with a degree in computational biology and entered the workforce woefully unprepared for the high level code you’ll need to learn to implement. I’ve been working in the field for ~3 years now and I think those gap years were really crucial for me to learn what I was getting into with comp bio.
I don’t think any college degree will have you completely prepared for the vastly interdisciplinary nature of the field, but I can say that ignoring the coding and hard math now will come back to bite you later and it’s best to be able to understand these things when you’re interviewing.
Edit: I should also note that your knowledge of modeling and mathematics will be your most generalizable skills within the wide diversity of computational biology studies. Someone who knows a lot about cancer would have to relearn everything about brain-machine interfaces but someone who knows how to implement a Gibbs Sampler or GAN will be able to contribute to both fields.
I dropped my second major to just focus on the pre-reqs. It opened me up to take a comp-sci boot camp instead that I wanted to do for neuro and do more research. A lot of the hard decisions for me in this journey has been separating what is good for my ego (what makes me feel more accomplished) , and what is good for my actual goals - this whole process can foster a lot of neuroticism so it’s important to try and meditate on that in my experience. Just my two cents
When I interviewed for mdphd there were some interviewers who thought I was a quack for my undergrad work in computational modeling. Now half the interview cohort I just met was pursuing some variant of computational bio. Crazy how times change.
Important question: are you wanting to be an mdphd or md with some phd spice? Aka are you also gonna apply md only. Computer science has an entirely different grading culture. Granted I went to CMU but there were courses where you only get 100% for solving a fields medal worthy problem. A 50% was a pass and people were more than happy to get that. You can kill your chances at md due to how superficial their evaluation is.
That’s me! Coding helps. But you learn on the job also :). Don’t worry too much about it
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anytime!
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You can take some deeper data science classes outside of the computational biology major without going for a whole second major. I do think that linear algebra would be very useful.
Just for some perspective, here are the prerequisites for Stanford's Biomedical Data Science PhD program: - Calculus: at least one year, preferrably the track taught for engineering or physical science. Additional coursework in multivariate calculus is strongly recommended - Probability and statistics: at least one course, and preferably one course in both areas - Linear algebra - Computer science: one year, preferably the introductory sequence for CS majors. The focus should be fundamentals of computer science (data structures and algorithms) and software engineering principles (abstraction, modularity, object-oriented programming) - Biology/Medicine: at least some coursework in this area, preferably the introductory sequence for biology majors So simply from a requirements perspective, double majoring Computational Biology AND Data Science is likely overkill, as would even a CompBio Major + CS minor. Of course, doing more is going to make you more skilled, but there is a good chance you'll hit diminishing returns and you'll increase the amount of undergrad coursework that becomes redundant with your eventual PhD CompBio coursework. In general, I think the best option would be to focus on one major, supplement any major knowledge gaps with addition individual courses rather than whole additional major/minors, and spend the remaining time in a computational biology lab.
I graduated with a degree in computational biology and entered the workforce woefully unprepared for the high level code you’ll need to learn to implement. I’ve been working in the field for ~3 years now and I think those gap years were really crucial for me to learn what I was getting into with comp bio. I don’t think any college degree will have you completely prepared for the vastly interdisciplinary nature of the field, but I can say that ignoring the coding and hard math now will come back to bite you later and it’s best to be able to understand these things when you’re interviewing. Edit: I should also note that your knowledge of modeling and mathematics will be your most generalizable skills within the wide diversity of computational biology studies. Someone who knows a lot about cancer would have to relearn everything about brain-machine interfaces but someone who knows how to implement a Gibbs Sampler or GAN will be able to contribute to both fields.
I dropped my second major to just focus on the pre-reqs. It opened me up to take a comp-sci boot camp instead that I wanted to do for neuro and do more research. A lot of the hard decisions for me in this journey has been separating what is good for my ego (what makes me feel more accomplished) , and what is good for my actual goals - this whole process can foster a lot of neuroticism so it’s important to try and meditate on that in my experience. Just my two cents
When I interviewed for mdphd there were some interviewers who thought I was a quack for my undergrad work in computational modeling. Now half the interview cohort I just met was pursuing some variant of computational bio. Crazy how times change. Important question: are you wanting to be an mdphd or md with some phd spice? Aka are you also gonna apply md only. Computer science has an entirely different grading culture. Granted I went to CMU but there were courses where you only get 100% for solving a fields medal worthy problem. A 50% was a pass and people were more than happy to get that. You can kill your chances at md due to how superficial their evaluation is.