Data Science vs. Computer Science: Which Path to Choose

Ekene Eze · Improve this Guide

If you love uncovering patterns in data and using those insights to solve real-world problems, data science might be the right fit for you. On the other hand, if you’re drawn to creating systems, writing code, and building the tools that power today’s technology, computer science could be your path.

From my experience working on projects in both fields, I’ve seen how they overlap and where they differ. As a data scientist, you’ll focus on analyzing complex data using math, programming, and problem-solving skills. Your work might include building models to extract meaningful insights from data, enabling you to identify patterns, predict trends, detect fraud, or improve recommendations.

Computer science, in contrast, focuses on understanding how computing systems work. You’ll write code, design algorithms, and develop programs to solve problems. You might create web applications and software tools or even dive into artificial intelligence and cybersecurity.

Although these fields overlap in areas like programming, they cater to different interests and career goals. In this guide, I’ll walk you through what each field involves, the skills you need, and the opportunities they offer. By the end, you’ll have the clarity to make an informed choice about your future based on which path suits you best.

Data science vs. computer science: What are the key differences?

First, let’s look at this table that summarizes the differences. Then, we’ll discuss each one in more detail.

Characteristics	Data science	Computer science
Primary focus	Extracting insights from data	Building computer systems to perform tasks
Industries	Finance, healthcare, marketing, e-commerce	cybersecurity, gaming, education
Career paths	Data analysts, data scientists, machine learning engineers, data engineers	Software engineers, computer scientists, cybersecurity analyst
Key areas and specializations	Machine learning, data mining, data visualization, statistical analysis	Data structures, software development, artificial intelligence
Tools and technologies	Python (Pandas, NumPy), R, SQL, TensorFlow	Python, Java, C++, JavaScript, Git, Vs code, databases (MySQL and MongoDB)
Educational background	Mathematics, economics, computer science, data science, physics	computer science, mathematics, electrical engineering

Your primary focus The big difference between data science and computer science is their primary focus. If you choose data science, your role will involve extracting insights and helpful information from datasets. You’ll use different tools and techniques to interpret and analyze data. The aim is to help businesses make data-driven decisions. Netflix, for example, uses machine learning and data science to learn your viewing history. Data scientists then analyze data to predict what shows you’ll like and suggest movies.

In contrast, if you choose computer science, you’ll focus on building computer systems to perform tasks. You’ll create tools that make computers, web apps, data analysis, etc, work better. For example, a company like Google uses computer science to create algorithms that make search engines work. Computer scientists then create software systems and algorithms that will give you accurate results when you search online.

Your career path Computer science and data science have very different career paths. As a data science professional, you’ll use data to help make decisions and solve business problems. You’ll work in various industries, like finance and healthcare, and take on roles such as:

• Data analyst: Collect and analyze data to help businesses make intelligent decisions. For example, as a data analyst in healthcare, you would use data analysis to know which treatments work best for a specific illness.
• Data scientists: Build predictive models to predict outcomes. As a data scientist in finance, you’ll use data analysis to forecast how the stock market might go.
• Data engineers: Build and maintain data systems that enable data analysis.
• Machine learning engineer: Design and build machine learning models to solve everyday problems. As a machine learning engineer at a robotic car company, you’ll create models to spot people, roads, traffic lights, and other cars. You’ll be responsible for designing machine learning algorithms to help the car make fast decisions.

In contrast, as a computer science professional, you’ll build the tools data engineers use to get work done. You’ll also work in different industries, such as cybersecurity and gaming, and take on roles like:

• Software engineers: Build and maintain software systems, including gaming platforms like Steam.
• Computer scientists: Study computer systems’ theoretical and practical aspects to create applications.
• Cybersecurity analyst: Monitor and address possible security threats like data breaches.

Your key areas of specialization Data science focuses on machine learning, data mining, statistical analysis, and more**.** As a data scientist, you’ll use these specializations to understand trends and make better decisions.

In contrast, computer science focuses on artificial intelligence, data structures, and software development. As a computer scientist, you’ll study these fields to create the tools that data scientists use.

The tools and technologies you’ll use Choosing data science requires using different tools and technologies to manipulate data. These tools include machine learning libraries (TensorFlow) and languages like Python and R. Python libraries like Pandas will help you with data manipulation and NumPy for math calculations. As a data scientist, you’ll also use big data technologies like Hadoop to work with huge amounts of data.

On the other hand, computer science focuses on software development. As a computer scientist, you’ll use programming languages like Python, C++, and JavaScript to create different web applications. You’ll also use tools like React, Git, databases (MySQL and MongoDB), and IDEs (VS code) to write and test codes.

What education do you need for computer and data science careers?

Getting a computer science degree gives you a solid foundation for software development. It will help you understand computer science principles and how to code and solve issues. Examples of these principles include programming languages, data structures, and operating systems. As a computer science graduate, you’ll have a strong foundation that will help you land various tech jobs.

Many universities offer specializations in data science within their computer science programs. Others treat data science as a separate program, recognizing it as a field in its own right. They teach you all about machine learning, data visualization, statistics, and more. These data science programs combine ideas from computer science, mathematics, and other fields. At the end of the program, you’ll get a data science degree and the necessary skills to get a tech job.

Studying at a university is not the only way to get a data and computer science education. Many computer science students and aspiring data scientists attend boot camps and learn via certifications or online tutorials. For instance, you can find many data science courses on roadmap.sh and learn at your own pace.

Learning this way is more flexible and can work with all kinds of schedules and ways of learning. For instance, you can juggle work and study at the same time, which is much harder to do when you study at a university. If you go for a traditional degree like a data science degree, you’ll need to invest more time, but you’ll get a broad education.

With these educational paths in mind, a common question arises: D**o you need a computer science degree to pursue a career in data science?** The answer is a simple no.

A computer science degree is not always required for computer and data science positions. It can help you start your career in data science, for example, by giving you a solid programming foundation. However, you can study other fields like mathematics, physics, and economics and still be a successful data scientist. You can also go through boot camps and online tutorials on data analysis, machine learning, and data visualization to gain the necessary skills.

Also, having some practical skills and constant practicing will give you more experience. When practicing, work on personal and open-source projects and build your portfolio to increase your chances of getting a job. Create time to attend meetups and join online communities to chat with other professionals.

What are the essential skills you’ll need?

Computer science and data science have a broad range of specialized skill sets. Some of these skills are relevant in both fields, and others are unique.

Even though computer science and data science are not the same, they do have some skills in common. These shared skills make it easy to switch between the two fields. Yes, it’s true; you can transition from computer to data science and vice versa. The following are some examples of these shared skills:

• Programming skills: Programming skills are a crucial common ground for both fields. Knowing how to code to solve problems as a computer or data scientist is important. The process involves learning programming languages like Python, having a deep understanding of data structures, and more. It lets you do software development (computer science) or data manipulation (data science). However, it is worth noting that some tasks, like data visualization, do not require coding.
• Solving problems: As a computer or data scientist, it is important to be able to solve problems. This helps you to create software, fix errors, and understand data.
• Mathematics and statistics: Knowledge of mathematics and statistics will help you to solve problems in both fields. Computer science uses math principles in areas like algorithms and data structures. They will help you as a computer scientist make fast and better software and solve coding issues. As a data scientist, you use statistics to analyze data and machine learning.

What skills do you need for computer science?

• Programming languages: Computer scientists use programming languages to give instructions to computers**.** Knowing one or more of these programming languages (JavaScript, Java, etc.) will help you to be successful in this field.
• System architecture: Knowledge of system architecture will help you build reliable computer systems**.**
• Software development methodologies: Software methodologies help you to plan and manage software projects. These methodologies (agile, scrum, etc.) will help you collaborate better with others when creating software.

What skills do you need for data science?

• Machine learning techniques: Machine learning techniques are important skills in data science. A deep understanding of machine learning techniques will help you build prediction models. Among the many examples of these techniques are clustering and decision trees. They allow computers to make predictions and recognize patterns without instructions from anyone.
• Data analytics: To get into the data science field, you must know data analytics. It is the starting point for many data science tasks, e.g., building machine learning models. Data analytics allows you to understand data, find patterns, and draw conclusions.
• Data visualization techniques: These help present data results in clear visual stories. As a data scientist, they allow you to show patterns that might be hard to see in raw numbers in pictures or graphs. You do this using tools like Tableau, Matplotlib, or Power BI. Some examples of these techniques include bar charts, histograms, and scatter plots.

How to choose between data science and computer science

Let’s get into the details to help you decide which field fits you best. Choosing between both fields involves understanding your strengths, interests, and the job market.

Your strengths and educational background The path you choose boils down to what you’re into, what you’re good at, and your educational background. Computer science might be a good fit if you’re into how computers work and creating software systems. An academic background in computer science or engineering also makes you a good fit.

If you like finding hidden patterns in data and solving problems, then data science could be for you. You’ll also be a good fit if you’ve studied mathematics, economics, or computer science.

Earning potential and industry demand Many people ask: Which pays more, data science or computer science? Well, both fields pay high salaries and are in high demand in the industry.

Data scientists are in high demand across various sectors, like healthcare and finance. According to Indeed, the average salary for a data scientist in the United States (USA) across all industries is $123,141.

Computer science professionals like software engineers and software developers are also in demand. According to Indeed, computer scientists in the USA make around $121,452 a year on average.

How much money you earn can depend on where you live, your field, and your skills. roadmap.sh provides computer and data science resources to help you improve in both fields.

FAQ: Data science or computer science?

The following are answers to common questions to help you start your career as a computer and data scientist.

Is data science harder than computer science? Data science and computer science are challenging fields in their different ways. So, it is difficult to say one field is harder than another. The difficulty level varies based on personal viewpoints, interests, and capabilities.

Is data science still in demand in 2025? Yes, data science is still in demand in 2025. The US Bureau of Labor Statistics predicts a 36% increase in data science jobs from 2023 to 2033.

How long does it take to complete most computer science programs? Computer science programs like a bachelor’s degree often take four years to complete. Master’s programs take one to three years, depending on your pace or the school. Bootcamps and online certifications, however, may take less time.

Is data science more focused on mathematics or computer science? Both fields are important parts of data science—you can’t have one without the other! It uses statistics and mathematical concepts to analyze data and computer science for handling data and building models. The balance may vary depending on the specific role, project, or focus within data science.

What Next?

Deciding between data science and computer science does not need to be a difficult task. Figure out what works best for you by thinking about what you like, what you’re good at, and what you want to achieve.

Also, you don’t have to limit yourself to just one field. Many people use data and computer science skills to solve problems daily. So, it is very normal to be good in both fields.

However, if you’re a beginner, focus on improving at one before learning another. roadmap.sh provides roadmap guides for you to learn computer and data science. Both roadmaps contain resources and everything you need to get started.