- 1.CS programs require 40-60% programming coursework plus heavy math foundation including calculus, discrete math, and statistics
- 2.Core curriculum covers data structures, algorithms, computer systems, software engineering, and theory of computation across 8-12 courses
- 3.Most programs allow 2-3 specialization tracks like AI/ML, cybersecurity, or systems programming starting junior year
- 4.Expect 3-5 major programming languages (typically Python, Java, C++, JavaScript) plus markup languages and database query languages
12-15
Core CS Courses
3-5
Programming Languages
6-8
Math Courses
5-8
Specialization Options
Computer Science Program Overview: What to Expect
A Computer Science degree typically requires 120-128 credit hours over four years, with 40-50% dedicated to computer science coursework, 25% to mathematics and science, and the remainder to general education and electives. The curriculum is highly structured in the first two years, covering foundational concepts before allowing specialization.
Modern CS programs follow ACM/IEEE curriculum guidelines, ensuring graduates have core competencies in programming, algorithms, systems design, and computational thinking. The curriculum balances theoretical computer science (algorithms, complexity theory) with practical software development skills needed in industry.
Most programs are ABET-accredited, which standardizes core requirements while allowing flexibility in electives and specializations. This means CS graduates from any accredited program will have comparable foundational knowledge, making the degree portable across employers and graduate schools. See our Computer Science degree rankings for top programs.
Math and Science Foundation: The Heavy Lifting
Computer Science is fundamentally mathematical, requiring strong quantitative skills. Expect 6-8 math courses that form the theoretical foundation for advanced computer science concepts.
- Calculus I & II: Essential for algorithms analysis, machine learning, and graphics programming
- Discrete Mathematics: Logic, set theory, graph theory—the mathematical language of computer science
- Linear Algebra: Matrix operations for graphics, machine learning, and data science applications
- Statistics & Probability: Required for AI/ML track and data analysis coursework
- Physics I & II: Many programs require physics to develop problem-solving and mathematical maturity
- Advanced Math (Optional): Calculus III, differential equations for specialized tracks like scientific computing
Students struggling with math often find CS challenging. Programs typically require C or better in all math prerequisites. If math isn't your strength, consider Data Analytics or Information Technology degrees with less mathematical rigor.
| Prerequisites | Key Topics | ||
|---|---|---|---|
| CS I: Programming Fundamentals | Freshman Fall | None | Variables, loops, functions, basic algorithms. Usually Python or Java. |
| CS II: Object-Oriented Programming | Freshman Spring | CS I | Classes, inheritance, polymorphism, data encapsulation. Java or C++. |
| Data Structures | Sophomore Fall | CS II, Discrete Math | Arrays, linked lists, trees, hash tables, algorithm complexity analysis. |
| Computer Systems/Architecture | Sophomore Spring | CS II | CPU design, memory hierarchy, assembly language, system calls. |
| Algorithms | Junior Fall | Data Structures, Calculus I | Sorting, searching, graph algorithms, dynamic programming, complexity theory. |
| Software Engineering | Junior Spring | Data Structures | SDLC, testing, version control, project management, team collaboration. |
| Database Systems | Junior Fall/Spring | Data Structures | Relational databases, SQL, normalization, database design, transactions. |
| Operating Systems | Senior Fall | Computer Systems | Process management, memory management, file systems, concurrency. |
| Computer Networks | Senior Fall/Spring | Computer Systems | TCP/IP, HTTP, network protocols, distributed systems basics. |
| Theory of Computation | Senior Spring | Discrete Math, Algorithms | Formal languages, automata theory, computability, complexity classes. |
Source: ACM Computing Curricula 2020
Programming Languages You'll Master
CS programs typically teach 3-5 programming languages strategically chosen to illustrate different programming paradigms and prepare students for diverse career paths. The specific languages vary by program, but the concepts transfer across languages.
- Python: Usually the first language due to readable syntax. Used throughout the curriculum for algorithms, AI/ML, and data science courses
- Java: Object-oriented programming concepts, enterprise development patterns. Common choice for data structures and software engineering courses
- C/C++: Systems programming, memory management, performance optimization. Essential for computer systems and operating systems courses
- JavaScript: Web development, asynchronous programming. Increasingly common for software engineering and full-stack development tracks
- Assembly Language: Low-level programming for computer architecture courses. Teaches how computers actually execute instructions
- SQL: Database query language for database systems courses. Essential for any data-related career path
Advanced courses may introduce specialized languages like R for statistics, MATLAB for scientific computing, or Haskell for functional programming concepts. The goal isn't language mastery but understanding how to learn new languages quickly—a crucial software engineering skill.
Specialization Tracks: Choose Your Focus
Most CS programs allow specialization starting junior year through elective clusters or formal tracks. Choose based on career goals and interests discovered through core coursework.
| Track | Core Courses | Career Paths | Median Salary |
|---|---|---|---|
| Artificial Intelligence/Machine Learning | Machine Learning, Neural Networks, Computer Vision, Natural Language Processing | AI Engineer, Data Scientist, Research Scientist | $140,000 |
| Cybersecurity | Network Security, Cryptography, Ethical Hacking, Security Architecture | Security Analyst, Penetration Tester, Security Architect | $102,600 |
| Software Engineering | Advanced Software Design, Testing, DevOps, Agile Methodologies | Software Engineer, DevOps Engineer, Technical Lead | $130,160 |
| Systems Programming | Compilers, Distributed Systems, High-Performance Computing | Systems Engineer, Backend Engineer, Infrastructure Engineer | $125,000 |
| Human-Computer Interaction | UI/UX Design, Usability Testing, Interface Design | UX Engineer, Product Designer, Frontend Developer | $95,000 |
| Data Science/Analytics | Data Mining, Statistical Analysis, Big Data Systems | Data Scientist, Data Engineer, Business Intelligence Analyst | $126,830 |
Which Should You Choose?
- You excel in mathematics and statistics
- You're interested in cutting-edge research and development
- You want the highest earning potential in tech
- You enjoy working with large datasets and complex algorithms
- You're interested in protecting systems and data
- You enjoy puzzle-solving and thinking like an attacker
- Job security is a priority (high demand field)
- You want clear industry certifications and career progression
- You enjoy building applications and user-facing products
- You want the broadest career options and mobility
- You're interested in startup environments and product development
- You prefer collaborative development and agile methodologies
- You're interested in how computers work at a fundamental level
- You want to work on infrastructure and backend systems
- Performance optimization and efficiency appeal to you
- You're considering graduate school in computer systems
Capstone Projects and Portfolio Development
Most CS programs culminate in a capstone project—a semester-long software development project that demonstrates mastery of curriculum concepts. This becomes the centerpiece of your professional portfolio.
- Individual Project: Solo development of a substantial application showcasing technical skills and project management
- Team Project: Collaborative development simulating real-world software engineering with 3-5 team members
- Industry Sponsored: Real projects from local companies providing industry experience and networking opportunities
- Research Project: Academic research with faculty advisor, ideal for students considering graduate school
Strong capstone projects often lead directly to job offers or graduate school opportunities. Successful projects typically solve real problems, demonstrate clean code architecture, and include proper documentation and testing. Start planning early—the best projects begin with simple prototypes built throughout the program.
Beyond the capstone, build a portfolio throughout your studies. GitHub repositories with clean, documented code are essential for job applications. See our guide on building a portfolio that gets hired for specific strategies.
3.2
Average GPA for CS Majors
65%
Graduation Rate
4.5 years
Time to Degree
85%
Job Placement Rate
What Jobs Can You Get With a CS Degree?
Computer Science graduates have the broadest career options in technology, with strong job growth and high median salaries across multiple industries.
Career Paths
Software Engineer
SOC 15-1252Design, develop, and maintain applications and systems. Most common CS career path.
Data Scientist
SOC 15-2051Extract insights from large datasets using statistical analysis and machine learning.
AI/ML Engineer
SOC 15-1299Build and deploy machine learning models and AI systems at scale.
DevOps Engineer
SOC 15-1299Automate software deployment, infrastructure management, and system reliability.
Cybersecurity Analyst
SOC 15-1212Protect organizational systems and data from security threats and breaches.
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Taylor Rupe
Full-Stack Developer (B.S. Computer Science, B.A. Psychology)
Taylor combines formal training in computer science with a background in human behavior to evaluate complex search, AI, and data-driven topics. His technical review ensures each article reflects current best practices in semantic search, AI systems, and web technology.