Data Science vs Computer Science: Which Degree to Choose?

Data science and computer science are two of the hottest degrees in America right now. Both lead to high-paying careers in tech, both are in enormous demand, and both involve heavy doses of math and programming. But they're not the same degree — and choosing the wrong one could cost you years of career momentum.

Here's a clear-eyed comparison to help you decide.

What's the Difference?

Computer Science is the foundational study of computation, algorithms, software systems, and how computers work. It's been around for decades and covers everything from operating systems to artificial intelligence.

Data Science is a newer, interdisciplinary field that combines statistics, programming, and domain knowledge to extract insights from data. It's more focused on analysis and less on building software systems.

	Computer Science	Data Science
Core Focus	Software, algorithms, systems	Statistics, ML, data analysis
Math Emphasis	Discrete math, linear algebra	Statistics, probability, calculus
Programming	Multiple languages, systems-level	Python, R, SQL-focused
Key Courses	OS, compilers, databases, networks	ML, data mining, visualization, stats
Degree Availability	Nearly every university	Growing but still limited

Salary Comparison

Both degrees lead to high salaries, but there are important differences:

Computer Science

• Starting salary: $75,000 - $95,000 (NACE 2025 data)
• Mid-career salary: $120,000 - $160,000
• Top earners (FAANG/Big Tech): $200,000 - $400,000+ (total compensation)
• BLS median for software developers: $127,260

Data Science

• Starting salary: $70,000 - $90,000
• Mid-career salary: $110,000 - $150,000
• Senior/Lead data scientists: $150,000 - $250,000+
• BLS median for data scientists: $108,020

Computer science has a slightly higher ceiling, primarily because software engineering roles at top tech companies offer massive total compensation packages. But data science salaries are excellent and growing rapidly.

Job Market in 2026

The BLS projects:

• Software developers: 25% growth through 2032 (much faster than average)
• Data scientists: 35% growth through 2032 (one of the fastest-growing occupations)

Both fields have far more job openings than qualified candidates. You won't struggle to find work with either degree.

Career Flexibility: CS Wins Here

This is the biggest differentiator. A computer science degree is one of the most versatile degrees in existence. CS graduates can work as:

• Software engineers
• Data scientists (yes, CS grads can do this too)
• Machine learning engineers
• Product managers
• DevOps/cloud engineers
• Cybersecurity analysts
• Quantitative analysts (finance)
• Technical founders / entrepreneurs

Data science is more specialized. While data scientists are in high demand, the degree doesn't as naturally prepare you for software engineering, systems design, or infrastructure roles.

Think of it this way: a CS major can become a data scientist relatively easily, but a data science major would need to fill significant gaps to become a software engineer.

Which Degree Should You Choose?

Choose Computer Science if:

• You want maximum career flexibility
• You're interested in building software products
• You want to keep your options open between different tech roles
• You enjoy systems-level thinking and problem solving
• You want to work at FAANG or top tech companies

Choose Data Science if:

• You love statistics and working with data
• You're more interested in analysis than building software
• You want to work in a field that intersects with business, healthcare, or science
• You prefer Python/R/SQL over C++/Java
• You're interested in machine learning and AI research

The Safe Bet

If you're unsure, computer science is the safer choice. It's more established, more widely recognized by employers, and more versatile. You can always specialize in data science through electives, a minor, or on-the-job learning.

Data science is the right choice if you know you want to work with data and you prefer statistics over systems programming. Just understand that you're choosing a narrower path — albeit one that pays extremely well.