Mathematics and Computer Science.

Degree Overview

Mathematics and Computer Science (CIP 30.0801) is a high-rigor interdisciplinary STEM degree that blends two of the most powerful “languages” for solving modern problems: mathematical reasoning and computational thinking. If computer science teaches you how to build software and systems, mathematics teaches you how to prove why they work, how efficient they are, and what limits exist. This program is designed for students who want to operate at that deeper level—where code is not just written, but analyzed, optimized, and formally understood.

For a degree search site, this CIP code is a strong match for students who want “the best of both worlds”: a career-ready technical skill set plus a foundation in logic, proof, modeling, and abstraction. It is especially relevant for people interested in algorithms, artificial intelligence, machine learning, cryptography, data science, quantitative finance, scientific computing, and research-level software engineering. In simple terms, this is a “power user” version of computing: you learn to program, but you also learn the mathematical structures that make computation possible.

What Is a Mathematics and Computer Science Degree?

A Mathematics and Computer Science degree is an interdisciplinary program that integrates core computer science topics (programming, data structures, algorithms, systems) with core mathematics topics (calculus, discrete math, linear algebra, probability, proof-based reasoning). The defining feature is balance: instead of treating math as a few prerequisites, the program treats math as a co-equal pillar—meaning students spend more time on formal reasoning, modeling, and theory than many standard computing majors.

This degree is often structured for students who want:

• Stronger algorithmic and proof skills than a typical CS path
• More computing and real-world technical application than a pure mathematics major
• Preparation for graduate school (MS/PhD) or advanced technical roles
• A broad foundation that stays relevant even as programming languages and tools change

Because the program builds both theory and implementation, it often appeals to students who enjoy asking “why” and “how” at the same time. Instead of only learning how to use a method, you learn how to derive it, analyze it, and adapt it.

What Will You Learn?

Students in this program learn to reason about computation with mathematical precision. You’ll develop the ability to define problems clearly, choose or invent an algorithm, prove it works, and evaluate its efficiency. That combination is extremely valuable in fields where correctness and performance matter—like security, finance, machine learning, and large-scale systems.

Core Skills You’ll Build

Most students graduate with skills such as:

• Algorithm design and analysis—choosing the right approach and proving its time/space efficiency
• Proof-based reasoning—writing logical proofs and using formal methods to validate conclusions
• Discrete mathematics and logic—the foundation behind computing, graphs, and network thinking
• Data structures mastery—trees, graphs, hash tables, and structures that enable scalable software
• Computational modeling—representing real-world processes using mathematical or simulation models
• Probability and statistical thinking—uncertainty, inference, and the backbone of data science and ML
• Optimization mindset—finding best outcomes under constraints (resources, time, risk, cost)
• Programming fluency—writing clean code while understanding deeper tradeoffs in design and performance

Topics You May Explore

Exact course names vary by school, but common topics include:

• Discrete Mathematics: sets, logic, combinatorics, graphs, and proof techniques used in algorithms
• Algorithms: sorting, searching, dynamic programming, greedy methods, and complexity analysis
• Linear Algebra: vectors and matrices used heavily in machine learning, graphics, and scientific computing
• Calculus and Multivariable Calculus: change, optimization, and continuous modeling
• Probability and Statistics: randomness, distributions, inference, and modeling real-world uncertainty
• Theory of Computation: what problems are solvable, what’s efficient, and what’s fundamentally hard
• Numerical Methods: solving mathematical problems with computation (approximation, stability, error)
• Cryptography or Number Theory (at some schools): the mathematics behind secure communication
• Programming Languages / Compilers (optional track): understanding how code is parsed and executed

What Jobs Can You Get With This Degree?

This degree is broadly employable because it builds both practical and theoretical strengths. Employers often value graduates who can reason precisely, work with complexity, and build scalable solutions.

Common job paths include:

• Software Engineer (Algorithm/Systems Focus): building efficient backends, infrastructure, and performance-critical software
• Data Scientist: using statistics and computing to extract insights, build predictive models, and interpret data
• Machine Learning Engineer: implementing models, optimizing training pipelines, and deploying ML systems
• Security or Cryptography Engineer: designing secure protocols, encryption systems, and threat-resistant architecture
• Quantitative Analyst (Quant): applying probability, modeling, and coding to finance and risk
• Operations Research Analyst: optimization for logistics, scheduling, pricing, and resource allocation
• Computational Scientist: simulation and modeling in physics, chemistry, biology, or engineering contexts
• Research Assistant / Graduate Student: preparing for advanced study in CS, math, AI, or applied research

Where Can You Work?

Graduates can work anywhere complex problems exist and computation matters:

• Tech companies (software, cloud, platforms, infrastructure)
• AI and data-driven organizations (analytics, ML platforms, research groups)
• Cybersecurity and defense (secure systems, cryptographic tools, threat modeling)
• Finance and quantitative trading (modeling, pricing, optimization, risk analysis)
• Healthcare and biotech (bioinformatics, medical data science, modeling)
• Scientific and engineering labs (simulation, numerical computing)
• Government and public sector (data modeling, systems analysis, research)

How Much Can You Earn?

Earnings vary by role, region, and experience, but this degree tends to map to high-paying career tracks because it supports advanced technical work.

Typical ranges (often higher in major tech hubs):

• Entry-level software/data roles: commonly $75,000–$105,000
• Software Engineer (mid-career): often $110,000–$170,000+
• Machine Learning Engineer: frequently $120,000–$190,000+
• Quantitative Analyst: often $120,000–$200,000+ (sometimes higher with bonuses)
• Security/Cryptography-focused roles: commonly $100,000–$180,000+

Is This Degree Hard?

Yes—this is typically a high-difficulty, high-reward major. The challenge is that you don’t just learn concepts; you must justify them. Many courses require proofs, rigorous problem sets, and precision thinking. Students who struggle with abstraction may find it demanding at first.

However, for students who enjoy structure, logic, and deep understanding, this degree can feel energizing rather than draining. It teaches a “mental toolkit” that makes future learning easier—because once you truly understand the foundations, you can adapt to new tools, frameworks, and technologies faster.

Who Should Consider This Degree?

This program may be a great fit if you:

• Like math and programming and don’t want to give either one up
• Enjoy proving why something is true, not just memorizing formulas
• Want strong preparation for AI, data science, algorithms, cryptography, or research
• Prefer challenging problems that require deep focus
• Want a degree that remains valuable even if today’s programming trends change

How to Prepare in High School

To prepare, focus on both math and computing fundamentals:

• Take AP Calculus (or the highest math available) to build comfort with abstraction and problem-solving
• Take AP Computer Science or any coding course that teaches structured programming
• Practice logic and discrete thinking through puzzles, competitive programming, or math team
• Build small projects to learn debugging, iteration, and real-world constraints
• Strengthen writing and communication—explaining technical ideas clearly is a major advantage in interviews and teamwork

Mathematics and Computer Science (CIP 30.0801) is a degree for students who want to be more than “tool users.” It prepares you to be a builder who understands the foundations—someone who can handle complexity, prove correctness, and create efficient solutions in a world increasingly driven by algorithms, data, and automation.