Computational Biology Major: Overview Guide and Thoughts
Computational Biology Major: Overview Guide and Thoughts
I graduated Spring 2025 with Computational Biology (CB) and wanted to share my insights for anyone in or considering the major. The post is long but I try to cover everything you’d need to know from someone who’s been through it and I was inspired by this super helpful post years ago that could use an update + provide a fresh perspective. For context, I’m on the PhD route so that’s where I’ll have the most input on.
1. What is Computational Biology?
Basically, biology generates a lot of data these days so there’s value in people who specialize in analyzing it and building models. CB prepares you to do that.
Some notes:
It’s a very research-focused major because a capstone thesis is required to graduate
I’ll use comp bio/bioinformatics interchangeably for simplicity, but there’s technically nuances between them
The name used to include “Systems Biology” but this half wasn’t emphasized well (tldr: you study biological components together).
2. Comp Bio Timeline
1: Complete pre-major You need a >2.7 GPA in pre-major courses to declare it
2: Get into major You declare a concentration (Biological Data Sci, Bioinformatics, Dynamic Modeling) in your application but really it doesn’t matter which you choose b/c it doesn’t matter for career prospects. Choose whichever overlaps with classes you’re already interested in. No one interviewing me has ever asked about my concentration.
3: Complete “core” and “concentration” classes You don’t necessarily need to be in the major in order to start these classes, but the only bottleneck would be enrollment restrictions for certain majors. One workaround is declaring another major/minor that gets first-pass priority in your desired course, then switch to CB later.
4: Complete 2 “capstone” research courses Highly recommend finding a UCLA lab to join before your senior year since it’s lowkey an unwritten requirement for these courses.
5: Graduate
3. Pre-Major Courses
Classes I took but aren’t required are marked with (parentheses)
Lower Divs
LS7A, 7B, 7C (+7L)
The fundamentals you learn in 7AB (central dogma, genetics) show up often in the biology upper divs so make sure you understand the material. 7L is an easy A: complete the pre-labs, attend lab, submit the writeup
PHYS 5A, 5B, 5C
Take the 5 series over the 1 series unless you’re genuinely interested in physics. The 1 series is the engineering version and demands more time. The 5 series was still hard for me though, especially 5C. Try to start by Spring of freshman year since seats are competitive and the professor matters.
CHEM 14A, 14B (+14C, 14D)
I took the entire 14 series (except labs) because I wanted to take biochem. 14AB are manageable if you took AP Chem. 14CD were rough, but OChem isn’t necessary for bioinformatics, it just adds extra context especially if you do mol bio-related research. CS31, 32
Take these offseason from the CS majors b/c they lower the difficulty for non-CS backgrounds. I took CS31 w/ Stahl and CS32 w/ Smallberg in winter and spring, respectively. I can’t speak on what the courses look like now since I took both before ChatGPT. Don’t rely on AI to get through the projects because you’ll struggle in the long term. I had no prior coding experience and still use fundamentals I learned like polymorphism, data structures, classes. Both teach C++, which I haven’t used since but it’s a strict language, which forces you to have good coding fundamentals.
LS 30A, 30B, MATH 33A, 33B, STATS 10 (+61)
I took LS30AB because I thought it was interesting as a freshman but in hindsight it wasn’t necessary. The series covers mathematical modeling in the context of biological systems so it’s a decent intro to systems biology and dynamic modeling if you’re into that. Grading is generous and the coding labs aren’t very useful imo since you’re mostly running single lines of pre-written code, but they give you a basic foundation if you’ve never coded before.
MATH 33AB covers linear algebra and differential equations. Pay attention because topics like matrices, eigenvectors, linear equations come up in upper divs and are fundamental to ML and DS. Difficulty is professor-dependent, and I had Wang for 33B who’s notoriously easy.
MATH 61 was the hardest lower div for me, but also one of the most useful. It’s proof-based math, which most students haven’t done before. It’s challenging but essential if you want to do anything CS-related. Focus on set theory, combinatorics, and graph theory.
I substituted AP Stats for STATS 10, but this isn’t allowed anymore. STATS 10 is supposedly an easy class though.
C&S BIO 10
The most immediately useful lower div. You learn basic Python, R, and Unix. Super low-pressure class and the lectures cover current bioinformatics research, which gives you a good idea of the current field. Nadel teaches it and he’s great.
Upper Divs
Probability + Statistics: MATH 170E + BIOSTATS 100A
MATH 170E is useful because probability distributions come up constantly in bioinformatics. STATS 100A is essentially the same so just take whichever is more convenient.
BIOSTATS 100A was an easy A but didn't teach me much; it's mostly applying basic statistics in public health contexts.
Gateway Courses: C&S BIO M184, 185
- M184 is a seminar where you attend a weekly talk and write a paragraph about what you learned.
- 185 is project-based and is dependent on what you put into it. Wollman teaches it and guarantees you an A so you could half-ass it, but if you actually put in the effort, it gives you amazing practical experience. You form a group with 4 other CB majors, find a paper you like, and do a follow-up coding project based on the paper. It’s a good opportunity to meet other CB majors and I met some of my closest friends in the major here.
Biological Modeling: C&S BIO M150
The class was a fever dream to me imo. Make friends because the take-home midterm and final took my five-person group 10+ hours within the 24-hour window. I skipped most lectures and mostly just reviewed slides weekly, but I was fortunate enough to have friends I could ask if I needed help. The content got super confusing halfway through the quarter.
Capstone: C&S BIO M187, 199
I recommend starting SRP 199 as early as possible because it counts towards your GPA (up to 8 quarters). My PI was chill and would give me an A regardless, so I stacked like 6 quarters of it.
M187 is a cool class. I took it as a sophomore so I met a lot of CB upperclassmen. You spend the quarter developing a research poster and 10-minute presentation based on your UCLA research to show at a symposium (and you get a free poster).
LS 107
Required to access most life science electives, including MCDB and MIMG courses. Without it, you're mostly limited to EEB offerings. The format mirrors the 7 series and it's not a hard class.
MCDB 138, 165A, 144, 187AL
I took these while planning to double major w/ MCDB (but ultimately didn’t). The core MCDB courses (138, 165A, 144) will prepare you well for connecting bioinformatics data to real biological mechanisms.
165A (Cell Bio) was my favorite because it teaches experimental design like how to identify good controls, which is useful even if you’re focused on bioinformatics.
144 (Molecular Bio) is valuable if you’re interested in genomics, mol bio, or biophysics
138 (Developmental Bio) is dense and complex, but I ended up in a neurodevelopmental lab postgrad so I still reference my notes from the class. Not necessary imo unless you’re interested in development or STEM cell work.
I had Rigeur for 138 and 144, and Coller for 165A - both are amazing lecturers. I recommend taking 144→165A→138.
- 187AL is a genomics dry lab and a free A. You annotate a plant genome all quarter and finish with a writeup and presentation. I took it just to have something to do my last quarter, but I’m not really interested in plant work. Pellegrini teaches it but he’s a dry lecturer.
CHEM 153A
I learned a lot, but wish I could’ve taken more biochem. Biochem is another course that’s useful to take if you wanna have a deep biology background. 153A covers macromolecule structures and metabolism. If you wanna do research in cancer or immunology, metabolism is essential to know. Lannan is a funny and engaging prof and his tests are extremely fair.
Comp Bio Electives: C&S BIO M178, M130
M178 (taught by Hoffmann and Deeds) covers biological circuitry and a bit of immunology. Most of the work is coding homeworks in Jupyter notebooks, which is just filling in Python code
M130 (taught by Shah) is about image processing in biology with a similar homework format, but uses MATLAB. MATLAB is annoying but not hard to learn.
CS Electives: CS 180, CM121, M146
CS180 is useful for SWE since it covers algorithms you’ll need for LeetCode and tech interviews. I took it over the summer w/ Bautista, which made it manageable.
M146 introduces machine learning, but be aware that the concepts taught are like 20 years old at this point (but not useless). We only got through basic neural nets by the end. If you want to get into AI (or whatever’s trendy), you’ll need to take additional courses or self-learn. It’s proof-heavy and essentially a math class with applications. If you want applied data science, take M148 instead. I recommend taking w/ Sankararaman.
CM121 is “Introduction to Bioinformatics” but is really an introduction to genomics/sequencing (a subset of bioinformatics). We covered DNA/RNA-seq, pseudoalignment, graph theory, dimensionality reduction, probability. There’s a bias towards covering DNA/RNA and not much on proteomics or evolutionary biology. Pimentel was engaging when I had him.
4. Career Outlooks
PhD in Comp Bio/Bioinformatics
Even if a comp bio PhD is your goal, I’d still recommend building a strong foundation first. For the majority, you’re better off majoring in:
MCDB, Biochemistry, MIMG, or BioE with a Bioinformatics/Data Science Eng Minor, or
CS, Math, or Statistics, while taking upper-level biology electives
The problem with CB’s curriculum is that:
It doesn’t give you enough biological intuition to ask good questions
It doesn’t give you enough math to build new models
I’m not saying it’s impossible with a CB degree, but you’ll need extra effort through your lab research since classes alone won’t prepare you. Many hardworking people in my graduating class are now in computational biology PhD programs. Looking back, I would major in MCDB for the biology and done computational work in my research lab.
Tech/SWE
It’s possible, but an uphill. The people I knew who landed FAANG/unicorn jobs followed a pattern:
- SWE/DS/Analytics internship at biotech company
- 2nd internship at traditional tech company
- FT offer in tech
If tech is your goal and biology doesn't interest you, switch to something like CS or Math of Comp - don't waste time on biology courses you won't need.
Biotech/Pharma Industry
Be warned: most entry-level bioinformatics and comp bio roles require an MS or PhD. BS-level openings are mainly research associate or bench-heavy technician roles. Since CB has no required wet lab courses, you'll lack hands-on experience (PCR, cell culture, etc.) unless you gained wet lab experience elsewhere.
Pre-Med
The math and CS courses make it harder to maintain your GPA, and the required courses don't cover all MCAT topics. Easier majors like psychobio or human bio are better if you want to maximize GPA.
Healthcare/Business/Consulting
I know some CB majors pursue these, but I don't personally know any, so I can't say much here.
5. Other Notes
Research at UCLA
If you’re looking for bioinformatics research, just cold email professors, there’s plenty of advice out there on how to do it. I recommend emphasizing CS/Stats/Math courses you’ve taken at UCLA if you’re looking to join a computational lab here.
There’s big names in the department like Alex Hoffmann, Paul Boutros, Eleazar Eskin, Matteo Pellegrini, but you’re also not limited to CB-affiliated faculty.
Gripes
Counseling is essentially nonexistent - advising appointments were nearly impossible to get, though I'm not sure if that's changed. You're largely left on your own, relying on upperclassmen for course planning advice. If you're more biology-focused, I'd recommend seeing Maggie from the MCDB department instead.
While you can take a diverse set of classes, most are hard to get into because they’re locked to other majors and CB is usually an afterthought. For example, CB isn’t guaranteed first-pass priority for MIMG, MCDB, PHYSCI, COMSCI, EC ENGR classes. You’ll have to rely on department PTEs.
Departmental Scholars Program
You stay an extra year for a BS+MS in 5 years. It sounded impressive as a freshman but I wouldn’t recommend it now. From what I’ve heard, you retain undergrad status in the master’s portion so you miss out on the benefits of being a full graduate student, and the program isn’t that rigorous overall. My own PI in the CB department also discouraged the +1 MS. This is just what I heard so take this with a grain of salt. If you want to pivot to tech, a MS in CS/ML/DS is better.
Clubs
There are comp bio focused clubs but I wasn’t too involved with them. I do like Biomedical Engineering Society though - they run great technical projects that look good on resumes. I recommend Undergraduate Science Journal for those on the PhD route. There are also data science/CS clubs, but I don't have experience with those.
6. My Own Journey
The major worked well for me because I wanted to do both bench and computational work and get a PhD. I graduated in 3 years, but I didn't use department resources much. What carried me was being self-driven and building a good network. The CS majors, bio majors, chem majors, etc. you meet throughout are more useful than any department resource.
The most important thing I did at UCLA was joining a faculty lab my freshman year. I started on the bench, moved to a computational project, then went back to bench work. Having both skills is rare at the undergrad level - bench experience sets you apart from pure computationalists, and coding skills set you apart from pure experimentalists. It shows in interviews, and it opened doors for me: an R&D internship at a large pharma company and a molecular biology PhD. I understand the majority of CB majors don’t want to do bench work, but don’t be scared to try it.
CB worked for me because I used its flexibility intentionally. If you're willing to do the same, it can work for you too.
7.Takeaways
Comp Bio is designed to be a PhD pipeline so it’s only worth choosing if you’re interested in research. That said, it’s a jack of all trades, master of none.
One underrated upside is that the major has weight on a resume. Computational Bio isn’t offered at most universities so it tends to turn heads in interviews in a way a generic CS or Bio degree won't. That said, resume flash only gets you so far.
If you don’t want a PhD, you risk graduating without enough depth for biotech roles or tech roles. The major has requirements from different departments, but it consequently spreads you too thin. Without actively tailoring your courses, you’ll likely have only surface-level knowledge on both the biology and computational sides. A CB bachelor’s as a terminal degree just won’t get you as far as you’d think.
For those seeking a PhD: you need to figure out how you learn best. If you learn better from structured coursework than hands-on research, I’d point you toward a different major. Most people shouldn’t specialize in bioinformatics at the undergrad level anyway. Instead, build a strong foundation in either biology or math/CS/stats first, then supplement with the other as needed.
Ultimately, the major as a whole is as fulfilling or as mickey as you make it.