undergradly.
Major · Multi/Interdisciplinary Studies

A STEM bridge for applied numerical computing

Computational Science (CIP 30.3001) is the applied bachelor's that blends scientific computing, numerical analysis, simulation modeling, and high-performance computing across physics, biology, engineering, and finance. About 425 students complete it across 50 institutions; tracked occupations include data scientists ($112,590, 33.5% growth) and computer occupations all other ($108,970), with national-lab and FAANG research-scientist roles gated behind a PhD.

Schools offering
50
Annual completions
425
Typical degree level
Associate's + Bachelor's
Median earnings (5yr)
—

About this major

Computational Science (CIP 30.3001) is the applied bachelor's that sits at the intersection of math, computer science, and a scientific or engineering domain. The CIP definition reads as scientific computing and its application — scientific visualization, multi-scale analysis, grid generation, numerical algorithms, high-performance parallel computing, and numerical modeling and simulation across science, engineering, and other disciplines where computation is the main instrument. Coursework typically combines applied mathematics (linear algebra, ODEs and PDEs, numerical methods, probability and statistics) with a CS core (data structures, algorithms, systems, parallel programming) and a domain track — physics and astronomy, climate and atmospheric modeling, computational biology, computational finance, or engineering simulation. The skill stack students actually leave with is Python (NumPy, SciPy, pandas, scikit-learn, JAX, PyTorch), C++ for performance code, CUDA, OpenMP, and MPI for parallelism, plus exposure to Fortran (still load-bearing at national labs and in legacy climate models), R, Julia, and the Linux/Git/SLURM DevOps baseline.

The major fits students who want simulation, HPC, or scientific research as their primary instrument rather than software engineering for its own sake. It rewards mathematical maturity and serious programming discipline more than either parent field alone. Day-to-day at the undergraduate level looks like problem sets in numerical analysis, programming projects that combine algorithmic depth with implementation correctness on parallel hardware, and a capstone that applies the toolkit to a real domain problem — climate, materials, drug discovery, computational fluid dynamics, or quantitative finance.

One grounded observation about scale and labor-market positioning: 425 students complete this CIP across 50 institutions, with Michigan State (57), Washington State (52), Rochester (42), UT Austin (31), Indiana (24), and University of Puerto Rico-Mayaguez (21) producing the largest cohorts. The Scorecard wage data is suppressed at this CIP's sample size, so the packet's earnings fields are null — but the BLS occupation tags give a realistic floor: data scientist ($112,590, 33.5% growth), computer occupations all other ($108,970, 8.2% growth), natural sciences manager ($161,180), and mathematical science occupations all other ($71,490). Bachelor's-only roles at HPC vendors (NVIDIA, AMD, HPE Cray, Intel, AWS HPC) and national labs (LLNL, LANL, Sandia, Oak Ridge, Argonne, NREL, NIST, PNNL) typically pay $70K-$95K entry and scale to $130K with strong programming. The research-scientist tier at FAANG (Google Research, Meta FAIR, NVIDIA Research, Microsoft Research, Apple ML, Isomorphic Labs) and top quant firms (Citadel, Jane Street, Two Sigma, DE Shaw) pays $200K-$400K+ but is gated behind a PhD. Aerospace and defense (NASA Ames/JPL/Goddard, Lockheed, Northrop, SpaceX, Anduril), automotive R&D (Tesla, Ford, GM), simulation vendors (Ansys, Siemens Simcenter, Altair, Dassault SIMULIA), and climate modeling (NOAA, NASA GISS, ClimateAi, Carbon Direct) round out the destination map.

Section 3 · Careers

Where this major leads

Occupations most often associated with this major, from the federal BLS+O*NET crosswalk. Job-growth projections and median wages are national.

Occupation Median wage Job growth Typical education
Computer occupations, all other $109k +8.2% Bachelor's degree
Data scientists $113k +33.5% Bachelor's degree
Postsecondary teachers, all other $78k +1.8% Doctoral or professional degree
Computer programmers $99k -6.0% Bachelor's degree
Natural sciences managers $161k +3.7% Bachelor's degree
Computer science teachers, postsecondary $97k +5.3% Doctoral or professional degree
Mathematical science occupations, all other $71k +4.0% Bachelor's degree
Section 4 · Earnings

Earnings at a glance

Median graduate earnings from the federal College Scorecard, 5 and 10 years out.

Earnings data is not yet available at this major's level of granularity. See the concentration hub for family-level figures.

Who this major is for

Good signs this major fits: you want simulation, HPC, or computational research as the instrument of your career and not just software engineering generally; you have strong calculus and linear-algebra foundations from high school or first-year college coursework and enjoy the kind of numerical-methods problems where convergence, stability, and floating-point error actually matter; you're willing to learn the parallel-programming stack (CUDA, OpenMP, MPI) and at least dip into Fortran for HPC contexts; you're drawn to a specific domain — climate, computational biology, materials, aerospace, quantitative finance — and want a credential that signals you can build models in that domain; you're either willing to ramp into a PhD (the path to research-scientist roles at FAANG, national labs, and top quant firms) or content with $70K-$130K HPC and simulation engineer roles directly out of the bachelor's; the small program footprint (425 completions across 50 schools) doesn't worry you; and the destination set — national labs, HPC vendors, aerospace, climate modeling, computational biology, and quant finance — actually excites you.

Reasons to pause: this credential is one of the higher-ROI bachelor's degrees if and only if it's paired with strong programming and math depth — without that depth, it underperforms a Computer Science (CIP 11.0701, 50,272 completions) or Applied Mathematics degree on labor-market placement, because employers can't tell the difference between a serious computational-science graduate and a generalist who took a few simulation courses. If your primary goal is industry software work without the research ramp, standard CS gives you the same $100K-$130K destinations with broader optionality. The wage data in the packet is suppressed because of small sample sizes, so headline ROI claims should be treated with caution — verify each program's published placement and graduate-school feeder rates before assuming national-lab or FAANG outcomes apply. The deepest-paying tier of this field (research scientist at FAANG, national labs, top quant firms, $200K-$400K+) genuinely requires a PhD in computational science, applied math, or a domain field — the bachelor's is a launchpad, not a ceiling but also not the destination. Programs that skimp on systems programming, parallel computing, or numerical-methods rigor leave graduates with a credential that doesn't deliver the outcomes the field promises; check the curriculum for explicit C++, CUDA, MPI, and PDE coursework before committing. The CIP cross-references Computational Mathematics (27.0303) and Computational Biology (26.1104) — if your interest is more theoretical or more biology-specific, those are better fits.

Section 6 · Where to study

Top colleges for Computational Science

Ranked by annual completions at the associate's or bachelor's level — a proxy for program scale.

College Location Assoc. Bach. Total
Michigan State University East Lansing, MI 0 57 57
Washington State University Pullman, WA 0 52 52
University of Rochester Rochester, NY 0 42 42
The University of Texas at Austin Austin, TX 0 31 31
Indiana University-Bloomington Bloomington, IN 0 24 24
University of Puerto Rico-Mayaguez Mayaguez, PR 0 21 21
University of Georgia Athens, GA 0 20 20
Modesto Junior College Modesto, CA 19 0 19
University of Arkansas Fayetteville, AR 0 18 18
Chapman University Orange, CA 0 16 16
Section 7 · Explore sideways

Related majors to explore

Computer Science

50,272 completions/yr

Biological and Physical Sciences

25,374 completions/yr

Mathematics, General

18,758 completions/yr

Information Science/Studies

10,334 completions/yr

Management Information Systems, General

7,648 completions/yr

Computer Programming/Programmer, General

5,205 completions/yr

Statistics, General

3,379 completions/yr

Cognitive Science, General

2,642 completions/yr
Section 9 · Frequently asked

Common questions

What does Computational Science actually involve day to day?
The CIP definition centers on scientific computing and its application: scientific visualization, multi-scale analysis, grid generation, data analysis, applied mathematics, numerical algorithms, high-performance parallel computing, and numerical modeling and simulation across science and engineering domains. Coursework typically combines applied math (linear algebra, ODEs/PDEs, numerical methods, probability), CS fundamentals (data structures, algorithms, parallel programming), and a domain track (physics, climate, computational biology, finance, or engineering simulation). Expect heavy programming in Python (NumPy, SciPy, JAX, PyTorch), C++ for performance code, and exposure to Fortran, CUDA, OpenMP, and MPI in HPC-flavored programs.
How is this different from Computer Science (11.0701) or Computational Math (27.0303)?
Computer Science (CIP 11.0701, 50,272 completions) is broader CS theory plus systems and software engineering — it's the default credential for industry software roles. Computational Mathematics (CIP 27.0303) is more pure-math heavy. Computational Science (30.3001) sits between them: it's applied numerical computing tied to a scientific or engineering domain. Choose CS if you want maximum optionality for software jobs; choose Computational Math if you want graduate study in pure or applied math; choose Computational Science if you specifically want to build simulations, run HPC code, or do scientific research with computers as your main instrument.
What jobs and employers actually hire bachelor's graduates?
Bachelor's-only realistic destinations cluster around HPC engineering, simulation engineering, and data-science roles paying $70K-$95K entry, scaling to $130K with strong programming skills. Named employers include national labs (LLNL, LANL, Sandia, Oak Ridge, Argonne, NREL, NIST, PNNL) for HPC and simulation engineer positions, HPC vendors (NVIDIA, HPE Cray, Intel, AMD, AWS HPC), aerospace and defense (Lockheed, Northrop, Boeing, NASA Ames/JPL/Goddard, SpaceX, Anduril) for CFD and structural simulation, automotive R&D (Ford, GM, Tesla, Rivian) for crash and aerodynamic simulation, climate modeling (NOAA, NASA GISS, ClimateAi, Carbon Direct), computational biology (Schrodinger, Recursion, Isomorphic Labs), and quantitative finance (Citadel, Jane Street, Two Sigma) for the strongest students.
Is the bachelor's enough, or do I need a PhD?
The bachelor's is enough for a real career — HPC engineer, simulation engineer, or data scientist roles at $70K-$130K depending on programming depth and employer. But the highest-paying tier of this field is research-scientist work at FAANG (Google Research, Meta FAIR, Microsoft Research, NVIDIA Research, Apple ML), national labs, or top quant firms, and that tier is gated behind a PhD in computational science, applied math, computational physics, or a domain field. PhD-tracked research scientist roles pay $200K-$400K+ at top firms and 5-10 years out at national labs. Treat the bachelor's as either an immediate entry to industry simulation work or a launchpad for a research-track PhD — many students at top programs (UT Austin, Michigan State, Wisconsin) feed directly into PhD pipelines.
What programming languages and tools are non-negotiable?
Python (NumPy, SciPy, pandas, scikit-learn, JAX, PyTorch) is the floor for almost every modern destination. C++ is essential for performance-sensitive simulation code and HPC roles. CUDA, OpenMP, and MPI are the parallelism stack — required for national-lab and HPC-vendor work. Fortran is still load-bearing at national labs and in legacy climate models — the CIP description honestly covers HPC, and Fortran is part of that reality whether you like it or not. Linux, Git, SLURM (job scheduler), and Singularity or Docker are baseline DevOps. R for statistics and Julia as an emerging community language are nice to have. Programs that skimp on the systems-programming and parallel side leave graduates underprepared for the highest-paying simulation roles.
Why is the wage data missing for this CIP?
The packet shows null for both 5-year and 10-year median earnings — this is common for smaller CIPs where College Scorecard's federal-aid-recipient earnings data hasn't been disaggregated. With 425 completions across 50 institutions, sample sizes at any single school often fall below the reporting threshold. Use the BLS occupation wages in the packet (data scientist $112,590, natural sciences manager $161,180, computer occupations all other $108,970) plus the typical-education tags as the realistic anchor. For specific schools — Michigan State (57 completions), Washington State (52), Rochester (42), UT Austin (31) — check each program's published placement and graduate-school feeder data; those four account for over 40% of national output and have meaningfully different destination mixes.
Is AI a threat or a tailwind for this major?
Largely a tailwind. The fastest-growing edge of computational science is machine-learning surrogates for traditional simulation — DeepMind's GraphCast for weather, NVIDIA Modulus for physics-informed neural networks, ML-on-simulation projects at Lawrence Livermore and Argonne — and these are exactly the kinds of problems Computational Science majors are trained to attack. The tracked occupation Computer programmers ($98,670, -6% growth) is the one negative signal in the packet, and it reflects general programmer-tier roles being commodified, not the simulation and HPC work this major actually feeds into. Strengthen ML and statistics depth alongside the numerical-methods core to catch the wage-growth lane.