Data Science vs. Computer Science: What's the Difference and Which Should You Study?

The short answer: the two fields differ in emphasis. Computer science focuses on computational methods and the systems themselves, from algorithms and software to the infrastructure that runs them. Data science focuses on using data to build models, find patterns, and support decisions. They share a large foundation and overlap heavily, but the center of gravity of the day-to-day work sits in different places.

So if you want to build software, design systems, and care about performance and architecture, computer science is the better fit. If you want to answer questions with data, build predictive models, and explain the results to the people who make decisions, data science is. The rest of this guide walks through the differences so you can tell which side you belong on.

At a Glance

Focus: CS is about computation itself, processing information efficiently, correctly, and at scale; DS is about the patterns in data and what they mean for a decision
Core skills: CS means algorithms and data structures, systems programming, software engineering; DS means statistics and probability, machine learning, data processing and visualization
Typical roles: CS leads to software engineer, systems architect, information security analyst; DS leads to data scientist, machine learning engineer, data analyst
Graduate study: a CS master's leans toward engineering depth; a DS master's leans toward modeling and analytical methods

What Is Computer Science?

Computer science is the science of computation: which problems can be computed, how to compute them efficiently, and how to turn a solution into reliable software. Its core topics include algorithms and complexity, operating systems, databases, networks, distributed systems, and software engineering practice. A computer scientist might be optimizing a service that handles millions of requests per second, or designing the platform that lets a data scientist's model run at all. Computer science is the underlying language of almost every technical role.

What Is Data Science?

Data science is an interdisciplinary field that combines statistics, programming, and domain knowledge to draw conclusions from data. The typical workflow runs from defining the question, through collecting and cleaning data, building a model, and validating the result, to explaining what it means to non-technical decision makers. In 2026 there is an added layer: machine learning and generative AI have become everyday tools, and a growing share of data science roles now touch model deployment, monitoring, and generative AI applications, blurring the line between data science and ML engineering.

Where the Two Overlap

Data science and computer science overlap heavily. Data science borrows programming, databases, and distributed computing from computer science, while also resting on statistics, mathematics, and domain knowledge. In the other direction, more and more computer science roles expect an understanding of data and models. That shared ground has produced a wave of fast-developing AI roles, such as machine learning engineer and AI engineer, which usually call for both software engineering and machine learning skills. If those roles are your goal, either path can get you there; the difference is whether you enter from the systems side or the data side.

What You Will Study

The clearest way to see the difference is to compare what two master's programs actually teach. Using the Stevens Online programs as the example:

Stevens M.S. in Computer Science: algorithms and data structures; systems programming and operating systems; database management systems; software architecture and enterprise software design; web and mobile application development

Stevens M.Eng. in Applied Data Science: mathematical and statistical foundations of data science; machine learning and deep learning; big data technologies and data pipelines; generative AI and management of AI technologies; a capstone project on real data

Both sides include programming, databases, and machine learning, but the emphasis usually differs: computer science leans toward algorithms, system design, and software engineering, while data science leans toward statistical modeling, data processing, and putting models to work on real problems.

Careers and Salary Outlook

U.S. Bureau of Labor Statistics projections for 2025 to 2035 show demand growing on both paths, at different rates:

Software developers: median annual wage of $135,980 (May 2025), projected growth of 10%
Computer and information research scientists: median annual wage of $140,300, projected growth of 22%
Data scientists: median annual wage of $120,230, projected growth of 35%, among the fastest of any occupation

Data scientists have the higher growth rate, while computer science occupations such as software development start from a much larger base, so even at a lower rate they generate a large number of opportunities: the BLS projects about 106,100 openings a year across software developers, quality assurance analysts, and testers, compared with about 24,800 a year for data scientists. The rapid growth in data science coincides with expanding demand for AI and analytics capability across industries. Neither path lacks opportunity; the question is which curve you want to be on.

How to Choose

If you like building things that run, and care about performance, reliability, and architecture, choose computer science
If you like finding answers in data and explaining them to the people who make decisions, choose data science
If you already program and are aiming at ML or AI roles, both paths work: choose data science to build the models, computer science to build the systems that run them
If you have written little code so far, start with a foundational course or a certificate before committing to a master's

Which Path at Stevens?

Stevens Online offers a master's degree on each path. The M.S. in Computer Science is built for professionals who want to go deep in software and systems; the M.Eng. in Applied Data Science is built for those who want to drive decisions with data and AI. Both are 30 credits, 10 courses, 100% online, at $800 per credit, and designed for working professionals. If you are not ready for a full master's, the Applied Data Science Professional Graduate Certificate builds Python, SQL, and machine learning foundations in 9 credits that stack into the MEADS degree.

Know which side you are on? Apply to the Stevens Online MSCS or MEADS program today, or schedule a call with an enrollment advisor.