Computer science vs data science: Which degree is right for you? | Top Universities

Computer science vs data science: Which degree is right for you?

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Aisha Khan

Updated Mar 22, 2023




From the latest iPhone model to ChatGPT, it’s clear that technology pervades our everyday lives. Despite high-profile layoffs and economic uncertainty over the last few years, demand for tech professionals remains strong across a range of critical sectors.  

Computer science and data science are often seen as the best subjects to study to enter the 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. 

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.  

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 level depending on which university you choose to attend.  

As an example of course content, the BEng in Computing at Imperial College London (ranked joint 15th for computer science in the QS World University Rankings by Subject 2023) requires first-year students to take modules such as calculus, linear algebra, discrete mathematics, logic and reasoning, and introduction to computer systems. Some programmes will also include introductory modules in data science.  

In contrast, data science is a relatively new subject in higher education which may be housed in engineering or mathematics departments. Most programmes are offered at postgraduate level for students who already have a first degree in relevant disciplines.  

For example, the master’s in data science at Harvard University (ranked fifth for data science) consists of courses in the following areas: data systems, visualisation, statistical machine learning, artificial intelligence, linear models, and time series and prediction.  

It’s also worth noting both subjects require an aptitude for mathematics, however, data science has a greater focus in statistics, especially when using algorithms to simulate future outcomes.  


You may be wondering what your options are for specialisation outside of your core studies. At the University of Toronto (ranked first for computer science in Canada and 12th overall), examples of electives include game design, web and internet technologies, computer vision, human-computer interaction, computational linguistics and natural language processing.  

Optional modules in data science programmes tend to be career-focused, equipping students with, for example, the necessary techniques to solve modern scientific problems. The master’s in data science at Carnegie Mellon University (ranked second for data science) offers the following: computational genomics, biostatistics, fundamentals of bioinformatics, and digital molecular design studio.  

Entry requirements 

While it’s not essential to have formally studied computer science at school, a qualification in mathematics is often required for admission, along with a minimum of one science subject for some top-ranked institutions.  

Entry requirements for data science don’t always specify mathematics, but it’s necessary to have studied a quantitative discipline, such as physics or economics. You’ll also need to demonstrate knowledge of probability, statistics and programming in your application.  

Applicants from non-traditional backgrounds may be considered if you have significant relevant work experience.  

Career outlook 

There are a variety of roles that are open to both computer science and data science graduates such as business intelligence analysts, machine learning engineers or data scientists.  

As the tech industry continues to grow, both degrees can help you build lucrative careers. According to Indeed, the average yearly salary for data scientists and software engineers in the US is US$120,103 and US$102,234 respectively. 

Relevant roles for computer science graduates may include: 

  • Software engineer 

  • Information security analyst 

  • Web developer 

  • Application/system developer 

  • Games developer 

Data science graduates may be more suited to the following: 

  • Data analyst 

  • Data engineer 

  • Data architect 

  • Analytics manager 

  • Statistician  

It’s important to work out what your interests and career intentions are when deciding which subject is more suited to you. If you’re more interested in developing complex software systems, products and tools to improve security, safety or effectiveness then computer science may be your answer.  

On the other hand, data science is probably more suitable if you’re interested in providing answers to big, strategic questions by helping companies achieve greater effectiveness through data and analytics. 

Either way, both subjects provide a unique opportunity to make modern life easier and create positive impact. 

So, computer science or data science?  

Here’s a quick of summary of what to consider before making your decision: 

Computer science 

  • Wider scope with more sub-areas 

  • Focuses on building and using computers efficiently 

  • Emphasis on computing fundamentals and software engineering 

  • Traditional subject offered at all levels of higher education 

  • A background in mathematics is essential 

Data science 

  • Narrower in focus 

  • Concerned with organising and processing data 

  • Emerging field with most programmes offered at master’s level 

  • Less important to have formally studied mathematics - a numerate subject may suffice 

  • Statistics-heavy 

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