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Key differences between data science & computer science

Data Science
Blog Date
December 28,

Data Science and Computer Science go hand in hand, but what makes them different? And what are their similarities? They share some principles and tools, but they ultimately have other areas of focus. You should know them when you enter the field. In this article, we will see what data science and computer science are and the difference between data science and computer science.

What is data science?

Data science combines mathematics, statistics, specialized programming, advanced analytics, Artificial Intelligence, and machine learning with expertise to cover actionable insights within the organizational data. 

Businesses can use these insights to guide decision-making and strategic planning. The accelerating volume of data sources and subsequent data has made data science one of the fastest-growing fields across every industry. So If you are wondering if it is good to spend two years in a data science course, then the answer is yes, as more organizations are increasingly reliant on data scientists who provide recommendations to entrepreneurs on improving their business outcomes. 

Data science career option

Finding the right job is important in life for most individuals. However, it needs to be clarified to choose career titles in computational data science. Also, the demand for data science is increasing day by day. Below are some common job opportunities that exist in this field:

  •  Data analyst

Data analysis is an entry-level job in Data Science. As a data analyst, you are given questions by the organization, and you have to answer those based on your skills in data mining, data visualization, probability, and statistics and the ability to present complex information in an easy-to-understand way by using a dashboard, graphs, and charts.

  • Data scientist

As a data scientist, you need the right experience dealing with extensive data. A data scientist’s many activities are similar to that of a data analyst. The new skill they need is machine learning to design, develop and evolve machine learning models to make more accurate predictions based on past real-time data. They generally work independently to find patterns of information that the management might not have found and help benefit the company.

  •  Data engineers

Data engineer responsible for creating and maintaining the Data Analytics infrastructure and pipeline of the company by using skills in advanced SQL System administration programming and scripting skills to automate many tasks.

Tools a data scientist should know

Although the data science process is a fairly certain stone, there are much like the scientific method, the tools that a data scientist uses are open to negotiation, but most data scientists primarily need to know 

  • SQL 
  • R, SAS
  • Python
  • Tableau 
  • Pyspark 
  • Docker
  • Kubernetes
  • Airflow or similar because you can apply these tools and languages to any business.

What is computer science?

Computing is part of everything that we do. It drives innovation, business entertainment, and education and solves complex and challenging problems. Computer science is the learning of computational systems and computers. It is also a broad field that includes everything from algorithms that make up the software to how software interacts with hardware and how well the software is developed and designed. 

Computer scientists use various mathematical algorithms, coding procedures, and their expert programming skills to study computer processes and data mining and develop new software and systems for the organization. 

Computer science career options

After completing a computer science degree, some of the most common jobs you might find are as follows: You can even work for the top 10 Data Science companies.

  •  Applications or system software developer

Software developers are creative individuals. They are responsible for designing, developing, and installing software systems. They should have software development skills, version maintenance, and an eye to catch small mistakes in the large code base. The quality of problem-solving and resolving issues in broken code is necessary for developers. Along with these technical skills required for software development, the person must communicate the findings to management and collaborate with other developers and testers.

  •  Computer hardware engineer

As we know, a computer system consists of two major elements: Software and Hardware. Computer hardware engineers design, test, and produce computers and components related to different subsystems and electronic hardware, such as monitors, keyboards, motherboards, USB devices, and other components.

  •  Web developer

Web developers have the same skills as software developers; however, they code for applications that run in the browser, meaning they need to know HTML, CSS, and JavaScript to develop the front and parts of the web application. Moreover, forming parts of the backend takes a lot of interaction with the database and the application’s business logic. It needs to know programming languages such as Python, PHP, Ruby, Java, etc. With the introduction of NodeJS, it is easier to write backend functionalities in JavaScript.

  •  Database administrator

The database administrator runs and maintains one or more database systems. The administrator usually specializes in storing and processing data in the database, which helps you with queries, triggers, and stored procedures and packages. They ensure the security and availability of the data to the users and the shareholders. After computer science, you can even opt for career options in computer system analysis, forensic computer analyst, information security analysis, etc. 

Similarities between data science and computer science 

The following are some commonalities between the two roles that you might anticipate:

  • Both require a working knowledge of the company’s data and an understanding of the business and its products. 
  • Both roles typically involve fluency in using Git or GitHub. 
  • Both generally follow a systemic approach to the scientific process. 
  • Both are expected to be technology leaders. 
  • Both roles typically require proficiency in one programming language. 
  • Both roles can begin in one role and switch to the other.

Key differences between data science and computer science

Key differences between data science and computer scienc

Data science and computer science are interconnected because large data problems require efficient and reliable computation. Still, there are some critical differences between computer science and data science. Below are some key differences between data science and computer science:

  • Data science uses software machines and devices to bring out data by processing data with software and computing devices, whereas computer science is more about software machines and devices.
  • Computer science has activities related to developing and creating computing storage and networking, while data science activities are related to understanding user and organizational behavior.
  • In Computer Science, one has to study computer architecture, software algorithm hardware, and software design and implementation. However, in Data Science, one must explore different data types, such as structured, unstructured, and machine learning algorithms, to predict future outcomes. 
  • Data science uses machine learning and other techniques that connect the computational issue in data science with the algorithmic matter. In contrast, in Computer Science, computer science is used in data science to understand digital patterns in structured and unstructured data and to simplify many difficult and complex analytical tasks. 
  • The computer science algorithmic approach focuses on the mathematical foundations of numeric computation. It gives the practitioners the tool to create efficient algorithms and optimize their results, whereas, in data science, you focus on the necessary skills of algorithms and algorithmic modeling. Students study the fundamentals of using different algorithms, data mining techniques, and machine learning. 


To sum up, computer science vs. data science are two fields that come under the same roof when getting them to apply for the use of technology. Both the areas that are now important are advancing in technical aspects and creating new opportunities and technologies with sophisticated processes to ease our lives. 
Suppose you want to become a data scientist or computer scientist. In that case, you can go through the Online Manipal website and check out our data science degree vs. data science certification course to help you understand it better.


Information related to companies and external organizations is based on secondary research or the opinion of individual authors and must not be interpreted as the official information shared by the concerned organization.

Additionally, information like fee, eligibility, scholarships, finance options etc. on offerings and programs listed on Online Manipal may change as per the discretion of respective universities so please refer to the respective program page for latest information. Any information provided in blogs is not binding and cannot be taken as final.

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