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Data Science vs Computer Science: Key Differences

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There are many different concepts that fall into the fields of technology and artificial intelligence. Two such concepts are data science and computer science, which are closely related. These two concepts are often viewed as the same, but they are not. The skills required to be a professional within these fields are also highly sought after. 

Before breaking down each one of these concepts, it’s important to recognize that data science is all-encompassing, and computer science is often incorporated into the process, and vice-versa.

Let’s properly define each one before diving deeper:


  • Data Science: An interdisciplinary field, data science relies on scientific methods, processes, algorithms, and systems to extract or extrapolate knowledge and insights from structured and unstructured data. Knowledge from data is then applied across a wide range of domains.
  • Computer Science: The study of computation, automation, and information, computer science covers both theoretical and practical disciplines. It is generally considered an area of academic research that is distinct from computer programming. 

Differences Between Data Science and Computer Science

When studying data, data scientists can extract meaning from it through a series of methods, algorithms, systems, and tools. This toolkit enables them to extract insight from both structured and unstructured data. Structured data is data that is highly specific and stored in a predefined format, while unstructured data involves many varied types of data stored in their native formats. 

Data scientists often use their skills to extract valuable insights about business or marketing patterns, which is why they are in high demand. They can help the business perform better by gaining deep insights into its processes and consumers. Data science is used not just in business, but also government and various other bodies. 

Today's field of data science owes a lot to the proliferation of smartphones and the digitization of our lives. Our world is full of incredible amounts of data, and more is produced each day. Computing power has also drastically increased over time while decreasing in relative cost, which has made cheap computing power widely available. By combining digitization and cheap computing power, data scientists are able to extract more insight than was ever possible. 

When it comes to computer science and professionals in the field, they deal mostly with software and software systems, including their theory, design, development, and application. 

Some of the principal areas of study within computer science include artificial intelligence, computer systems and networks, security, database systems, human computer interaction, vision and graphics, numerical analysis, programming languages, software engineering, and bioinformatics. 

Despite what many believe, programming is essential to computer science, but it is only one element that makes up the entire field. Computer scientists also design and analyze algorithms that solve problems and study the performance of computer hardware and software. The challenges they tackle can include everything from determining what problems can be solved with computers to the design of applications that perform well on handheld devices. 

Over the last 30 years, computer science has increasingly played a role in our everyday lives. But it has also had a large impact on other areas of science that now require the processing and analysis of vast datasets. Without computer science, we would not be able to do things like capture and interpret data. 

Here is a list of points that describe key differences between Computer Science and Data Science:


  • Computer science is the field of computations consisting of subjects like Data Structures and Algorithms. Data science comprises mathematics concepts like Statistics, Algebra, and Calculus.

  • Computer science helps us understand how processors are built and operate, as well as memory management in programming areas. Data science helps us understand how data can be used in valuable ways.

  • Computer science covers the utilization of computing machinery and its applications. Data science tells us how to extract information and knowledge from various data forms.

  • The sub-fields of computer science include computations, probabilistic theories, reasoning, discrete structures, and database design. The sub-field of data science includes simulation, modeling, analytics, machine learning, and computational mathematics.

  • Computer science is concerned with building and utilizing computers in the most efficient way. Data science is about how to safely handle data and extract insights.

  • Computer science involves scientific ways of finding a solution to a problem, Data science deals with finding ways to organize and process data. 

This is by no means an exhaustive list of the differences between the two concepts, but it covers some of the main ones. 


Role and Skills of a Data Scientist

Data scientists work closely with stakeholders and executives in an organization to understand what the goals are. They can then examine how data can be used to achieve those goals and push the business forward. 

Data scientists must be adaptable and flexible, always open to new ideas. They also need to be able to develop and propose innovative solutions, especially as fields like artificial intelligence continue to explode. 

Usually working with different teams, data scientists need to have an awareness of business decisions within different departments. This will enable them to focus their efforts on data projects that will play a critical role in the decision-making processes of the company. They should also have a deep understanding of the kind of data the company should be collecting and using. 

As the role of a data scientist continues to get more integrated into a business, they will develop a strong understanding of customer behavior and how data can be effectively used to improve an entire business from top to bottom. 

*If you are interested in developing data science skills, make sure to check out our “Top 7 Data Science Certifications.” 

Role and Skills of a Computer Scientist

Computer scientists are more involved with mathematics and conceptualizing computational-related challenges. Besides this, they also write code, develop websites, and create applications. The mathematical models developed by computer scientists are interactive tools that close the gap between machine and human. 

Professionals looking to succeed in the field need to be able to take theory and translate it into application. They must also be highly efficient in programming, with a deep knowledge of various programming languages.  

Teaching and computer skills are not all that make up a data scientist. They should also possess strong communication and teamwork skills, as they will not be working alone. 

The fields of data science and computer science are two of the most important in our data-driven world. They will only continue to become more advanced and lead to innovations across sectors. 

Alex McFarland is an AI journalist and writer exploring the latest developments in artificial intelligence. He has collaborated with numerous AI startups and publications worldwide.