Data Science or Computer Science: Which Tech Path Is Right for You?

At first glance, data science and computer science may seem like closely related subjects — but as career paths, they have pretty different focuses. 

Data science is about the cold, hard numbers, while computer science is generally more creative. Both are extremely important and widely used in today’s society, which makes them a good career path—and it doesn't hurt that both pay well, too! 

So, which should you aim for? We'll show you what you need to know about computer science and data science so that you can make an informed choice.

What is data science?

Data science is all about finding patterns and making sense of huge data sets. We’re talking about data sets so massive that they’re too much for any human to read through, and seemingly too random for us to make sense of.

Instead, computer systems run through the data to uncover patterns that you can analyze further, then pick and choose the information that's actually important. Data science relies on mathematical principles like statistics, linear algebra, and calculus, and makes use of algorithms and machine learning_{The Best Programming Languages for Machine Learning}.

What is computer science?

Computer science is exactly what it says on the box — the study of computers, how they work, and how we use them. It covers a lot of different things like algorithms, data structures, programming languages, operating systems, software engineering, and computer architecture.

Many careers in computer science are about engineering and developing new software and systems, and this can mean anything from fun video games and apps to important medical software. It’s also connected to the field of artificial intelligence, where engineers aim to create software that can run without receiving manual instructions.

What does a data scientist do?

Data science is an indispensable tool for both small companies and massive corporations. The companies behind the products we use collect all kinds of information, from what we buy to what buttons we click on or which ads we look at, and data scientists use these details to deduce all sorts of new insights. The demand from big corporations is a significant part of why data science is a good career choice_{Is Data Science a Good Career Choice in 2024?} right now.

It’s not all about big business, though. Data can be a powerful tool in all sorts of areas, such as social services, environmental conservation, education, and healthcare. It can help figure out what people or species need the most help, and the most efficient and effective ways to help them. No matter what the cause is, however, the skills and the process are largely the same.

First, they re-organize the data so it’s easier to analyze, and then they run it through various programs to find different patterns, trends, and anomalies. Their own intuitions and understanding of the results help them to figure out what to investigate next. 

When they discover something interesting, data scientists use charts and presentations to explain their findings to other people. Sometimes they discover unexpected things and use their data to try and convince those people to make a change, and other times, the data scientists are given specific questions to answer.

Answers are not the only thing data scientists can summon from data, though. They can also make predictions. By developing and tuning predictive models, data scientists can use current data to make predictions on new data. For example, regression analysis is used to predict house prices or create sales forecasts, and clustering algorithms are used in customer segmentation and recommendation systems.

What does a computer scientist do?

While “Data Scientist” is a specific job title that you’ll see lots of job listings for, a computer scientist refers more to a person with a certain set of skills, rather than a specific job. With these skills, you can work in a number of different fields, such as:

• Software engineering
• System architecture
• Artificial intelligence
• Cybersecurity
• Algorithm design
• Database management

Software engineers and developers are the people who write, test, and debug programs like games, apps, banking software, medical software, art tools, and so much more. They use various programming languages like Java, Python, and C++. Other computer scientists create and maintain the tools developers and engineers use, such as:

• Algorithm designers
• Artificial intelligence engineers
• Database managers
• Systems analysts

Then there are the roles that keep everything secure and working efficiently, like cybersecurity analysts or network architects.

What is the difference between data science and computer science?

Deliverables

One of the main differences between data science and computer science is the end result, also known as deliverables. Data science is about uncovering information, while computer science is primarily about making things.

You might make things as a data scientist too, but only to help you analyze data more effectively. The things you actually deliver are the insights you discover, not the tools you made to get the job done. Computer scientists, on the other hand, deliver the designs, software, and systems they made to solve whatever problems were put in front of them.

Mathematics

The skills you need for computer science and data science overlap in a lot of places, but they still have different focuses. 

For instance, an education in either computer science or data science will include mathematical principles, but data scientists are much more likely to use math on a daily basis. For computer scientist jobs like software development, math powers the tools they use but they often don’t have to think about it themselves very much. This can make data science harder than computer science for people who aren’t that comfortable with math, but not all data science roles are math-centric.

Data knowledge

Unsurprisingly, data scientists also need to know a lot more about data, how to store large amounts of it, how to organize it, how to analyze it, and how to visualize it. Computer scientists are more worried about data structures — how their programs arrange data so that the computer can retrieve and use it quickly and efficiently.

Business knowledge

Many data scientists also need to know a lot about how businesses run and what they need to succeed because it’s their job to help with these things. That’s why it’s common for data scientists to have business-related degrees since many learn about business first and delve into technical skills later. It's an approachable field that can be picked up later on.

Computer scientists, on the other hand, don’t need to worry about this as much. If they’re in the business of consumer products, they need to think about what the customer needs, but the rest of the business stuff is left to other departments.

Programming languages

Probably the biggest thing data science and computer science roles have in common is the use of programming languages. The Python programming language_{5 Python Best Practices From a TripleTen Expert}, for example, is a very common first language for people to learn and it’s widely used for both computer science and data science projects.

Choosing between data science and computer science

The best way to choose between data science and computer science is to consider your natural strengths and interests.

You might have never made a piece of software before, but do you enjoy making other things? You might not be a master of data yet, but do you have a personal interest in properly researched and informed decision-making? 

Thinking about these kinds of character traits is a great way to figure out what kind of work you could learn to be passionate about. It’s much easier to work hard at the technical aspects if you have a strong motivation driving you. 

On the other hand, having natural talent in technical areas isn’t as important as you might think if you don’t have a strong interest that pushes you to ask the right questions and drive projects in the right direction.

You might be wondering how our students made the decision. Well, it’s different for everyone. Samuel Luo_{Embracing Change: From Interior Design to Software Engineering} was drawn to software engineering because of the “high income and stable employment.”

For Evgeniia Unzhakova_{How an Immigrant Landed a Career in the US: Evgeniia Unzhakova’s TripleTen Story}, on the other hand, being a programmer wasn’t what she wanted. Instead, she turned to data science because “it’s a science between math and programming.” And that’s exactly what she was looking for.

Data science and computer science in action

Now that you know the basic differences between data science and computer science, how does that play out in the real world? Here are a few practical examples of how data science and computer science are used in various industries.

Healthcare

• Data science: Collecting and analyzing data for personalized treatment plans or medical image analysis for diagnosis
• Computer science: Developing healthcare information systems, electronic health records, and medical imaging technologies

Finance

• Data science: Building predictive models for credit scoring, fraud detection, and stock market forecasting
• Computer science: Designing banking software for transactions, risk management systems, and blockchain applications

Retail

• Data science: Extracting information on things like customer segmentation for targeted marketing, demand forecasting, and sentiment analysis of customer reviews
• Computer science: Developing e-commerce platforms, inventory management systems, and supply chain optimization algorithms

Salary comparisons in data science vs computer science

Finding work you can be passionate about is important. However, salaries are pretty important too. Let’s see how data science and computer science compare, from entry-level tech jobs_{Getting an Entry-Level Tech Job With No Experience: Why You’re an Asset} to the top salaries at the best-paying companies.

Data science

Total pay range: $112,000 - $194,000

Average salary (all years of experience): $146,000

Average salary (entry-level): $112,000

Average salary (senior level): $182,000

Top salary example: $233,000 - $353,000 (Roblox)

Computer science

Total pay range: $113,000 - $209,000

Average salary (all years of experience): $151,000

Average salary (entry-level): $81,000

Average salary (senior level): $170,000

Top salary example: $230,000 - $392,000 (Apple)

Job outlook

If you’re going to the trouble of breaking into a new industry, it makes sense to read up on its health and growth rates. Luckily for us, the United States government is keeping track of these things.

According to the U.S. Bureau of Labor Statistics, the number of data scientist jobs is projected to grow by 35% over the next eight years. This is much faster than the average growth rate which is around 3%. This outlook means the number of job opportunities for data scientists will hopefully keep increasing, providing good job security, flexibility, and variety.

For computer science professions, the overall outlook is pretty good too. Software developers are at 25%, database architects are at 8%, web developers are at 16%, and cybersecurity roles are at 32%. There are plenty of jobs to be scored in either field — all you need to do is ready up with the knowledge and the determination to land one of them.

Find your fit

By this point, you’re probably starting to realize which sounds more interesting to you — which means our job is almost done! Almost, because we do have one more way we can help you out.

We have a nifty little career quiz that can help you discover which tech career is best for you. Ready to see if data science or computer science are your perfect match? Take the quiz here!