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Computer science and data science are often seen as the best subjects to study to enter the fast-changing technology industry, but understanding which one to choose can be tricky, particularly when course material can overlap across both degrees.
So, if you’re wondering whether you should study computer science or data science, here are some key differences to consider before making your decision.
What is computer science?
In broad terms, computer science is the holistic study of computers which encompasses its design, architecture, software, algorithms and hardware.
Widely viewed as the ‘backbone’ of data science, computer science involves building and using computers efficiently and seeks to understand how software and programming languages work.
As a computer science student, you’ll learn how to solve complex problems, create innovative technologies, and develop the skills needed to shape the future of computing in industries ranging from tech and finance to healthcare and gaming.
What is data science?
Unlike computer science, data science is less concerned with how software operates, instead focusing on various types of data (structured, semi-structured or unstructured) in order to obtain meaningful insights from it.
As a multi-disciplinary subject, it involves data mining, machine learning and data analytics, and concentrates on the algorithms that detect patterns in data and predict future outcomes.
As a data science student, you’ll learn how to work with complex data sets, build predictive models, and turn raw data into actionable insights, making you a valuable asset in industries like finance, healthcare, marketing, and technology.
Course content
In terms of academic differences, computer science is a traditional degree which often sits in engineering departments. Study options are normally available at undergraduate, postgraduate and PhD and even professional diploma level depending on which university you choose to attend.
The curriculum generally covers a wide range of topics, including, but not limited to, programming, algorithms, data structures, computer architecture, operating systems, artificial intelligence, software engineering, and cybersecurity.
To enrol in a computer science degree, you will typically need strong math and problem-solving skills, along with a background in subjects like mathematics, physics, or computer science at the high school level.
Data science, on the other hand, is a newer field, often available at the postgraduate level, though some universities now offer dedicated bachelor’s degrees and diplomas. These programmes are often housed in computer science, mathematics, or data science departments.
The curriculum blends computer science, mathematics, and statistics, with core subjects like data mining, machine learning, statistical modelling, data visualisation, big data, and data ethics.
Data science programmes often have more specialised entry requirements, typically seeking students with a solid foundation in mathematics, statistics, or a related quantitative discipline.
While both subject areas require an aptitude for mathematics, it’s important to note that data science places a stronger emphasis on statistics.
Specialisations
Both computer science and data science offer a variety of specialisations that allow students to tailor their studies to their interests and career goals.
In computer science, specialisations may include:
Game design
Web development
Networking
Software engineering
Computer architecture
Artificial intelligence
Cybersecurity
These electives enable students to dive deeper into specific aspects of computing, preparing them for a range of industries and roles.
Data science, on the other hand, often offers more career-focused specialisations, providing students with the skills needed to tackle real-world challenges in fields like healthcare, finance, and business.
Specialisations might include machine learning, big data, data visualisation, bioinformatics, and statistical modelling. These options allow students to develop expertise in using data to drive decision-making and innovation across various sectors.
Career outlook
Both computer science and data science graduates can pursue a wide range of high-demand roles in the rapidly evolving tech sector. While there is some overlap, each field offers distinct career pathways that cater to different skill sets and interests.
For computer science graduates, potential roles include:
Software engineer
Information security analyst
Web developer
Application/system developer
Games developer
Data science graduates, on the other hand, are typically better suited for roles such as:
Data analyst
Data engineer
Data architect
Analytics manager
Statistician
When choosing between the two subjects, make sure to consider your interests and career goals. If you enjoy designing robust systems, developing software, or solving technical challenges, a computer science degree may be a better fit.
Alternatively, if you’re drawn to uncovering patterns, making data-driven decisions, and influencing strategy, data science could be the right path for you.
Both fields offer competitive salaries and strong job security, reflecting the high demand for skilled professionals. Regardless of the path you choose, both degrees provide opportunities to innovate, solve complex problems, and drive meaningful change in a data-driven world.
Here’s a quick of summary of what to consider before making your decision:
Factors to consider
Computer science
Data science
Scope
Broad, and covers many subfields like AI, game design, cybersecurity, software development, etc.
Narrower, focused on data collection, cleaning, analysis, visualisation, and predictive modelling.
Study level
Available at all diploma, undergraduate, postgraduate and PhD levels.
Often offered at master’s or postgraduate level, with some undergraduate options emerging.
Mathematical background
Strong foundation in discrete math, algorithms, and logic.
Heavy on statistics, but a general numerate background may suffice.
Skills you can expect to develop
Skillset can vary based on the specialisation path you decide to take on, but general skills include; programming, problem-solving, system design, debugging, and logical thinking.
Statistical analysis, data storytelling, critical thinking, data wrangling, machine learning.
Career pathways
Career paths can depend on your specialisation, but a few general options include; software engineer, systems architect, cybersecurity expert, web developer, ai specialist, game developer, database administrator
Career paths can vary based on focus, but general options include; data scientist, data engineer, machine learning engineer, analytics manager, business intelligence analyst, data analyst
This depends on your background and interests. Computer science is often considered more challenging for those without a strong foundation in programming, algorithms, and system design, while data science can be more demanding for those without a solid grasp of statistics and data analysis.
Both fields require analytical thinking, but computer science typically involves more theoretical problem-solving, while data science focuses on practical data insights.
There are a range of factors to consider, including your interests, career goals, math skills, and educational background. Choose computer science if you enjoy building software, solving technical problems, and understanding how systems work.
Opt for data science if you prefer working with data to find insights, make predictions, and drive decisions.
Yes, data science is a highly promising career with strong demand across industries like tech, finance, healthcare, marketing, and more.
As businesses increasingly rely on data to make strategic decisions, data science skills are becoming essential, offering high salaries, job flexibility, and opportunities for growth.
Yes, a computer science graduate can become a data scientist, especially if they have a strong foundation in programming, algorithms, and data structures. Building skills in statistics, machine learning, and data analysis can help bridge the gap.
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Computer science vs data science: Which degree is right for you?
Keshala Jayawickrama
Updated May 15, 2025Table of contents
Table of contents
Computer science and data science are often seen as the best subjects to study to enter the fast-changing technology industry, but understanding which one to choose can be tricky, particularly when course material can overlap across both degrees.
So, if you’re wondering whether you should study computer science or data science, here are some key differences to consider before making your decision.
What is computer science?
In broad terms, computer science is the holistic study of computers which encompasses its design, architecture, software, algorithms and hardware.
Widely viewed as the ‘backbone’ of data science, computer science involves building and using computers efficiently and seeks to understand how software and programming languages work.
As a computer science student, you’ll learn how to solve complex problems, create innovative technologies, and develop the skills needed to shape the future of computing in industries ranging from tech and finance to healthcare and gaming.
What is data science?
Unlike computer science, data science is less concerned with how software operates, instead focusing on various types of data (structured, semi-structured or unstructured) in order to obtain meaningful insights from it.
As a multi-disciplinary subject, it involves data mining, machine learning and data analytics, and concentrates on the algorithms that detect patterns in data and predict future outcomes.
As a data science student, you’ll learn how to work with complex data sets, build predictive models, and turn raw data into actionable insights, making you a valuable asset in industries like finance, healthcare, marketing, and technology.
Course content
In terms of academic differences, computer science is a traditional degree which often sits in engineering departments. Study options are normally available at undergraduate, postgraduate and PhD and even professional diploma level depending on which university you choose to attend.
The curriculum generally covers a wide range of topics, including, but not limited to, programming, algorithms, data structures, computer architecture, operating systems, artificial intelligence, software engineering, and cybersecurity.
To enrol in a computer science degree, you will typically need strong math and problem-solving skills, along with a background in subjects like mathematics, physics, or computer science at the high school level.
Data science, on the other hand, is a newer field, often available at the postgraduate level, though some universities now offer dedicated bachelor’s degrees and diplomas. These programmes are often housed in computer science, mathematics, or data science departments.
The curriculum blends computer science, mathematics, and statistics, with core subjects like data mining, machine learning, statistical modelling, data visualisation, big data, and data ethics.
Data science programmes often have more specialised entry requirements, typically seeking students with a solid foundation in mathematics, statistics, or a related quantitative discipline.
While both subject areas require an aptitude for mathematics, it’s important to note that data science places a stronger emphasis on statistics.
Specialisations
Both computer science and data science offer a variety of specialisations that allow students to tailor their studies to their interests and career goals.
In computer science, specialisations may include:
Game design
Web development
Networking
Software engineering
Computer architecture
Artificial intelligence
Cybersecurity
These electives enable students to dive deeper into specific aspects of computing, preparing them for a range of industries and roles.
Data science, on the other hand, often offers more career-focused specialisations, providing students with the skills needed to tackle real-world challenges in fields like healthcare, finance, and business.
Specialisations might include machine learning, big data, data visualisation, bioinformatics, and statistical modelling. These options allow students to develop expertise in using data to drive decision-making and innovation across various sectors.
Career outlook
Both computer science and data science graduates can pursue a wide range of high-demand roles in the rapidly evolving tech sector. While there is some overlap, each field offers distinct career pathways that cater to different skill sets and interests.
For computer science graduates, potential roles include:
Software engineer
Information security analyst
Web developer
Application/system developer
Games developer
Data science graduates, on the other hand, are typically better suited for roles such as:
Data analyst
Data engineer
Data architect
Analytics manager
Statistician
When choosing between the two subjects, make sure to consider your interests and career goals. If you enjoy designing robust systems, developing software, or solving technical challenges, a computer science degree may be a better fit.
Alternatively, if you’re drawn to uncovering patterns, making data-driven decisions, and influencing strategy, data science could be the right path for you.
Both fields offer competitive salaries and strong job security, reflecting the high demand for skilled professionals. Regardless of the path you choose, both degrees provide opportunities to innovate, solve complex problems, and drive meaningful change in a data-driven world.
Testimonials
So, computer science or data science?
Here’s a quick of summary of what to consider before making your decision:
Factors to consider
Computer science
Data science
Scope
Broad, and covers many subfields like AI, game design, cybersecurity, software development, etc.
Narrower, focused on data collection, cleaning, analysis, visualisation, and predictive modelling.
Study level
Available at all diploma, undergraduate, postgraduate and PhD levels.
Often offered at master’s or postgraduate level, with some undergraduate options emerging.
Mathematical background
Strong foundation in discrete math, algorithms, and logic.
Heavy on statistics, but a general numerate background may suffice.
Skills you can expect to develop
Skillset can vary based on the specialisation path you decide to take on, but general skills include; programming, problem-solving, system design, debugging, and logical thinking.
Statistical analysis, data storytelling, critical thinking, data wrangling, machine learning.
Career pathways
Career paths can depend on your specialisation, but a few general options include; software engineer, systems architect, cybersecurity expert, web developer, ai specialist, game developer, database administrator
Career paths can vary based on focus, but general options include; data scientist, data engineer, machine learning engineer, analytics manager, business intelligence analyst, data analyst
Long term trends
AI, robotics, blockchain, cybersecurity
Big data, AI, data driven decision making
Frequently Asked Questions
This depends on your background and interests. Computer science is often considered more challenging for those without a strong foundation in programming, algorithms, and system design, while data science can be more demanding for those without a solid grasp of statistics and data analysis.
Both fields require analytical thinking, but computer science typically involves more theoretical problem-solving, while data science focuses on practical data insights.
There are a range of factors to consider, including your interests, career goals, math skills, and educational background. Choose computer science if you enjoy building software, solving technical problems, and understanding how systems work.
Opt for data science if you prefer working with data to find insights, make predictions, and drive decisions.
Yes, data science is a highly promising career with strong demand across industries like tech, finance, healthcare, marketing, and more.
As businesses increasingly rely on data to make strategic decisions, data science skills are becoming essential, offering high salaries, job flexibility, and opportunities for growth.
Yes, a computer science graduate can become a data scientist, especially if they have a strong foundation in programming, algorithms, and data structures. Building skills in statistics, machine learning, and data analysis can help bridge the gap.
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