Bachelor of Science in Mathematics with Computer Science

4 Years On Campus Bachelors Program

Massachusetts Institute of Technology

Program Overview

MIT’s Course 18-C is an interdisciplinary program that combines the depth and logical rigor of mathematics with the practical applications of computer science. This degree equips students with a strong foundation in mathematical theory while developing the computational skills necessary for solving complex problems across industries such as technology, finance, data science, and research.

Core Curriculum Components:

Mathematics Core

  • 18.01: Single Variable Calculus

  • 18.02: Multivariable Calculus

  • 18.03: Differential Equations

  • 18.06: Linear Algebra

  • 18.701: Algebra I or 18.100A/B: Real Analysis

Computer Science Core

  • 6.100A: Introduction to Computer Science Programming in Python

  • 6.1010: Fundamentals of Programming

  • 6.1200[J]: Mathematics for Computer Science

  • 6.1210: Introduction to Algorithms

Restricted Electives (choose from advanced topics in mathematics or computer science)
Examples may include:

  • 18.701: Algebra I

  • 18.100C: Real Analysis

  • 6.1220[J]: Design and Analysis of Algorithms

  • 6.3700: Introduction to Probability

Communication-Intensive Subject (CI-M)
Students must complete at least one CI-M course within the Department of Mathematics, such as:

  • 18.100W: Real Analysis (CI-M version)

  • 18.200W: Principles of Discrete Applied Mathematics

Experiential Learning (Research, Projects, Internships etc.)

MIT’s Course 18-C strongly emphasizes experiential learning, integrating theoretical knowledge with real-world problem-solving. Students have numerous opportunities to apply their skills beyond the classroom through a variety of avenues:

  • Undergraduate Research Opportunities Program (UROP): Students are encouraged to participate in research projects alongside MIT faculty and researchers, often contributing to cutting-edge advancements in fields such as cryptography, machine learning, optimization, and computational biology. UROP fosters mentorship, collaborative inquiry, and independent investigation, allowing students to explore research interests early in their academic journey.

  • Capstone Projects and Independent Studies: The flexibility of the curriculum allows students to undertake independent study courses or project-based experiences, tailored to their academic and career goals. These projects enable students to delve deeper into specialized areas, integrating advanced mathematical theory with practical computing applications.

  • Interdisciplinary Collaboration: Given MIT’s collaborative academic culture, students in Course 18-C frequently work across departments—including electrical engineering, economics, biology, and mechanical engineering—applying computational and mathematical approaches to diverse challenges.

  • Competitions and Hackathons: Course 18-C students often participate in national and international competitions such as the Putnam Mathematical Competition, ACM International Collegiate Programming Contest, and MIT-hosted hackathons. These experiences sharpen problem-solving skills under pressure while fostering teamwork and innovation.

  • Internships and Industry Partnerships: MIT’s global reputation facilitates access to competitive internship opportunities with leading technology firms, financial institutions, research labs, and startups. Many Course 18-C students gain hands-on industry experience during the summer or academic year, working in roles that blend software development, data science, and quantitative analysis.

Progression & Future Opportunities

Graduates of MIT’s Mathematics and Computer Science (Course 18-C) program are exceptionally well-positioned for a wide range of professional and academic pathways. Employers highly value the program’s combination of analytical rigor, computational expertise, and problem-solving versatility.

Career Prospects Include:

  • Software Engineering and Development: Designing, building, and optimizing software systems across industries such as technology, healthcare, defense, and aerospace.

  • Data Science and Machine Learning: Developing predictive models, analyzing large-scale datasets, and implementing AI-driven solutions in sectors like finance, healthcare, and e-commerce.

  • Quantitative Finance: Applying mathematical modeling and algorithmic strategies in roles such as quantitative analyst, risk manager, or algorithmic trader at investment banks, hedge funds, or fintech firms.

  • Cybersecurity and Cryptography: Developing secure systems, encryption protocols, and cybersecurity defenses for government agencies and private-sector organizations.

  • Operations Research and Optimization: Designing and optimizing complex systems and processes in logistics, supply chain management, and industrial operations.

Many graduates pursue advanced studies in highly competitive graduate programs in mathematics, computer science, data science, artificial intelligence, economics, or computational science. Alumni frequently enroll in top-tier Ph.D. or master’s programs to deepen their expertise or pivot into interdisciplinary research fields such as bioinformatics, computational neuroscience, or financial engineering.

Beyond technical roles, Course 18-C also cultivates transferable skills in logical reasoning, structured problem solving, and quantitative analysis, opening doors to consulting, product management, public policy, and entrepreneurial ventures.

Overall, Course 18-C graduates emerge as adaptable, analytical thinkers prepared to lead in an increasingly data-driven, technologically complex world—whether in academia, research, industry, or beyond.

Program Key Stats

$61,990
$ 75
Aug Intake : RD 6th Jan EA/ED 1st Nov


8 %
No
Yes

Eligibility Criteria

AAA - A*A*A
3.5 - 4.0
38 - 42
90 - 95

1510 - 1580
34 - 36
7.0
90

Additional Information & Requirements

Career Options

  • Graduates of MIT’s Mathematics and Computer Science (Course 18-C) program are uniquely equipped to bridge computational problem-solving with mathematical and analytical reasoning
  • This interdisciplinary training unlocks diverse
  • high-impact career opportunities across industries
  • Technology & Data Science Data Scientist: Building predictive models and data-driven insights
  • Machine Learning Engineer: Developing AI algorithms and applications
  • Software Developer: Crafting innovative software solutions
  • Data Engineer: Designing scalable data pipelines
  • AI/ML Product Developer: Bringing AI-powered products to market
  • Finance & Quantitative Analysis Quantitative Analyst: Developing financial models and trading algorithms
  • Financial Engineer: Applying math to solve complex finance problems
  • Risk Analyst: Managing financial risks through data
  • Algorithmic Trader: Designing automated trading strategies
  • Investment Strategist: Using data analytics to guide investments
  • Consulting & Business Analytics Management Consultant: Advising on operations and strategy
  • Strategy Consultant (Tech Focus): Leading tech-driven transformations
  • Business Analyst (Data Focus): Providing insights from data
  • Economic Consultant: Applying econometric analysis to real-world issues
  • Operations Research Analyst: Optimizing business processes
  • Economics & Public Policy Economic Analyst: Conducting policy and market research
  • Policy Advisor: Using data to shape policy recommendations
  • Research Economist: Leading economic research projects
  • Data-Driven Policy Consultant: Informing policy with big data
  • Think Tank Researcher: Contributing to policy innovation
  • Product & Program Management Technical Product Manager: Overseeing tech product development
  • Data Product Manager: Managing data-focused product lines
  • Program Manager (Tech Firms): Coordinating complex initiatives
  • Digital Strategy Manager: Leading digital transformation projects
  • Entrepreneurship & Innovation Startup Founder (FinTech
  • EdTech
  • Data Analytics) Innovation Strategist Tech Entrepreneur Venture Analyst (Tech/AI VC)

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