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Computer science interacts with data, and data science interacts with computers. Some schools have schools have distinct computer science and data science programs. Others combine them into one.
So, what gives?
For someone not involved in the tech space in particular, it may be difficult to discern what the true differences are between the two closely related areas. And even for those in tech, sometimes there is a gray area as to what falls into each bucket.
The good news is that both data science and computer science are growing areas in terms of educational offerings and job opportunities. There is an increasing number of degrees, certifications, and bootcamps that are teaching the in-demand hard skills—like programming, machine learning, and data analytics—best catered to each area. Plus, tech salaries remain high; according to Dice, the average tech salary was $111,193 in 2023.
But the question remains: what’s the true difference between data and computer science? This piece will provide some insight.
What is data science?
As the name implies, data science focuses deeply on the collection, organization, and the extraction of data within the context of real-world problems, often in business. Fortune has a comprehensive guide diving more specifically into the intricacies of data science.
The study of data science often leads to careers as data scientists, data analysts, data engineerings, and more. Data-related occupations are growing faster than the national average for all U.S. jobs, with data scientists in particular growing at a rate of 35%, according to the U.S. Bureau of Labor Statistics. Plus, they have great salaries—averaging about $103,500 per year.
Data science skills center around programming languages like SQL, R, and Python as well as knowledge of statistics, mathematics, and AI.
Data science is a subdiscipline of computer science with offshoots in machine learning and big data statistics, says Jignesh Patel, professor at Carnegie Mellon University and co-founder of DataChat.
“Just as a doctor might specialize in pediatrics or surgery, a computer scientist might specialize in data science,” Patel says.
What is computer science?
Computer science is an even broader term that focuses on the study of computers, including software, hardware, networks, and AI.
“Careers in computer science cover a very broad range of functions such as software development, software engineering and architecture, computer security, development and management of databases,” says Mamdough Refaat, chief data scientist at Altair.
From a degree standpoint, computer science programs offer many different concentrations and specializations available that may include data science, cybersecurity, machine learning, and AI. Resultantly, those who study computer science are not necessarily siloed into a computer scientist job title. Software developer, systems analyst, and computer engineer are just a few examples.
Overall, demand for computer science-related roles are growing, and most of the time average salaries are near or within six-figures, based on data from the U.S. Bureau of Labor Statistics.
What is the difference between data and computer science?
The main difference between data and computer science is the level of focus. Computer science is a much more all-encompassing area of study—that even includes data science itself.
| Distinguishing elements | Computer science | Data science |
| Skills/knowledge | Networking, software, cybersecurity, algorithms, databases, AI | Machine learning, AI, deep learning, statistics, mathematics, spreadsheets, data management |
| Programming language | JavaScript, C++, HTML, and more | SQL, R, Python, SAS |
| Example job titles | Computer scientist, software developer, network/systems analyst | Data scientist, data analyst, data engineering, business analyst, business intelligence analyst, marketing analyst |
For computer science, the differentiating skills may include increased knowledge of computer networking, cybersecurity, and algorithms. Computer scientists may know a wide variety of programming languages like JavaScript, C++, HTML, and more. The subject also sometimes pairs well with other non-tech areas like physics and economics.
Data science, on the other hand, is much more of a niche subfield that intersects more closely with mathematics and statistics. The important skills include know-how of machine learning, AI, and deep learning. Plus the relevant programming languages are narrower and include SQL, R, and Python.
It is less popular to find a “computer science” certification or bootcamp programs. Instead, they may be more focused on a subject like IT, data analytics, AI, cybersecurity, and more.
“The two fields intersect in the fields of artificial intelligence, data management and programming,” Refaat tells Fortune. “However, computer science can be described as focused on the creation of programming environments and software in general, while data science is focused on exploring and using data.”
Since both fields are continuing to grow, Refaat adds that experts should expect a lifetime of learning—especially in a business problem-solving context.
“Employers in any industry seek data and computer scientists with deep understanding of their specific industry and its unique problems and areas of application,” he says. “Although basic skills in both fields are transferable between industries, having the business insight is a big advantage.”
