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Difference Between Computer Science and Data Science

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Introduction :

Computer Science can be referred to as the study of computers as well as computing concepts. It is basically the study of the processes which interact with data which is in the form of programs. It deals with the manipulation of the information by making use of various algorithms. Thus computer science deals with the study of both hardware as well as software and other components like networking and the internet. The hardware part of computer science deals with the study of the basic design of computers and their working process of it. The software part of computer science deals with the study of programming concepts as well as languages. Computer science also deals with operating systems and compilers. 

Computer Science is the study of computers and computational systems, including their design, implementation, and applications. It encompasses a wide range of topics, such as programming languages, algorithms, data structures, computer architecture, databases, operating systems, and software engineering.

Data Science is basically a field in which information and knowledge are extracted from the data by using various scientific methods, algorithms, and processes. It can thus be defined as a combination of various mathematical tools, algorithms, statistics, and machine learning techniques which are thus used to find the hidden patterns and insights from the data which helps in the decision making process. Data science deals with both structured as well as unstructured data. It is related to both data mining and big data. Data science involves studying historic trends and thus using its conclusions to redefine present trends and also predict future trends.

Data Science, on the other hand, is an interdisciplinary field that combines statistics, mathematics, and computer science to extract insights and knowledge from data. It involves collecting, processing, analyzing, and visualizing data to understand complex phenomena and make informed decisions. Data Science utilizes various tools and techniques such as machine learning, data mining, statistical modeling, and data visualization to extract insights from data.

Computer-Science-vs-Data-ScienceCareers in data science vs. computer science:

Computer science and data science are two related but distinct fields.

  1. Computer science is the study of computing technology, including hardware, software, and algorithms. It focuses on the design, development, and implementation of computer systems, software applications, and databases. Computer science covers a broad range of topics, including programming languages, data structures, algorithms, operating systems, computer networks, and artificial intelligence.
  2. Data science, on the other hand, is a multidisciplinary field that uses statistical and computational methods to extract insights and knowledge from data. It involves a combination of skills and knowledge from various fields such as statistics, computer science, mathematics, and domain expertise. Data science involves the entire process of data collection, cleaning, exploration, analysis, and interpretation.
  3. While computer science focuses on the design and implementation of computing technology, data science focuses on the use of computing technology to analyze and interpret data. Computer science provides the foundational knowledge and tools needed for data science, but data science goes beyond computer science to include statistical and mathematical methods for data analysis and interpretation.

In summary, computer science is a broad field that includes the study of computing technology and its applications, while data science is a specialized field that uses computing technology to extract insights and knowledge from data.

Since data science and computer science have different focuses, there are also different types of roles people in each of these areas of technology can pursue. Data science roles involve data collection and analytics specializations. These roles focus on working with a company’s data and interpreting data to help employers make decisions. Some data science roles include:

  • Data scientist
  • Database administrator
  • Data Architect
  • Data analyst
  • Statistician

Roles for computer science vary depending on a candidate’s skills and interests, but most jobs in this career area advance technology and make or maintain digital products for a company. Some computer science jobs also involve the hardware and physical products of a computer system. Computer science jobs include:

  • Software engineer
  • Web developer
  • IT analyst
  • Computer programmer
  • UI and UX specialist

Data science versus computer science job duties:

The job duties of people in a data science career often differ from the duties of those in computer science. Many data science job duties involve using data to solve problems or find opportunities for growth in a company, so data scientists may collaborate with stakeholders to help them better understand the problems and opportunities for which they’re searching. They also process the data they gather and present it in data models to stakeholders and managers so that everyone can see the trends and analyze the information.

A computer scientist’s responsibilities can change depending on their specialization. However, the general duties of a computer science career involve creating and maintaining technological tools like software programs and applications that help a company function or provide customers with a usable product. To do this, they write and test code in programming languages, look for bugs or issues with a piece of tech and design tools with their teams.


Both data science and computer science have a specific purpose in the technology field that allows them to accomplish smaller goals within the larger goal of improving a business. The purpose of studying and applying data science is usually to change the way a business functions by analyzing data and statistics to learn where the business can improve. Computer science’s purpose is to handle software, like programs and applications, and hardware, like physical computers and electronic tools, to make it easier for a company to perform well and create high-quality products.

Both disciplines use data and software to improve a company’s operations and create or maintain an organization’s technology more effectively. A data scientist’s work provides valuable insight and information to various departments in an organization, like finance, marketing and the teams creating actual products for customers. Computer science professionals often use the data from data science professionals, like information about the cost of running a server.


While computer and data scientists use overlapping skills like programming languages and data organization, each discipline uses a distinct set of abilities to complete unique functions within an organization. Data science professionals focus on statistical analysis with a background in advanced mathematical calculations. Data scientists also cultivate business intelligence, an understanding of the goals and strategies companies use to increase profits and improve their business model. Pairing technical skills like data visualization and programming allow data scientists to organize information and find trends used by company leaders to make important decisions.

Computer science professionals rely on programming skills through training in fundamental computer languages. They also focus on software development to design the information systems that help organizations operate efficiently. Computer scientists also use information technology skills to maintain processes like network security. Collaboration and problem-solving are some key soft skills computer scientists use to excel in their work.


The role of a computer science professional encompasses a broader scope of a business’s technical operations. They perform various tasks to maintain, repair, and organize a company’s software and hardware. Computer scientists oversee the architecture of computer systems within an organization to determine the best use of networks, data storage, and digital tools for a business.

Data scientists perform more business-facing roles within an organization. They act as analysts to mine data for useful trends and look for answers to complex questions. Data scientists cull vast amounts of information into sensible data useful for decision-making within an organization. Once they understand the data, these professionals communicate insights with other team members or company leaders either through reports, presentations or other communication channels. 

Below is a table of difference between Computer Science and Data Science: 

Computer Science Data Science
It is basically the study of the computational systems including both theory and applications. It is a field that uses mathematics, statistics,technologythatand various other tools to discover the hidden patterns in the data.
It is mainly used for the advancement and growth of technology. It is mainly used for the management of data and data analysis.
The advantage of using computer science is growth and development of technology. The advantage of using data science is handling and maintenance of large volumes of data.
It has been a branch of science for a really long time. It has evolved recently as a developing branch of science.
One after studying computer science becomes a computer science professional. One after studying data science becomes a data scientist or a data analyst.
Computer science is the super set of data science as it covers the entire technological field. Data science is a subset of computer science which involves the study of data and its analysis.
Its main benefit is technological advancement and improved speed and performance of technological devices. Its main benefit is easy management of data and reduction of data redundancy.
It is applied to nearly all the technical industries and companies. It is basically applied to the industries and companies where data is of quite a lot importance.
Focuses on computers and computational systems, including their design, implementation, and applications. Focuses on extracting insights and knowledge from data using statistics, mathematics, and computer science.
Covers topics such as programming languages, algorithms, data structures, computer architecture, databases, operating systems, and software engineering. Covers topics such as data collection, processing, analysis, and visualization, as well as machine learning, data mining, statistical modeling, and data visualization.
Provides the foundational knowledge and skills necessary to develop software and computational systems. Focuses on the analysis and interpretation of data to extract meaningful insights.
Deals with the efficient and effective use of computational resources to solve problems. Deals with the extraction of information from data to inform decision-making.
Has a focus on designing and developing new computational systems and software. Has a focus on analyzing existing data to uncover patterns and trends.
Has applications in a wide range of industries, including technology, finance, healthcare, and education. Has applications in a wide range of industries, including marketing, finance, healthcare, and social media.

Advantages of computer science:

  1. Job opportunities: Computer science skills are in high demand, and there is a wide range of job opportunities available in the field.
  2. Innovation: Computer science drives innovation in many industries, including healthcare, finance, and entertainment.
  3. Problem-solving: Computer science teaches valuable problem-solving skills that can be applied to a wide range of challenges.
  4. Versatility: Computer science skills are applicable to a wide range of fields and industries, from software development to robotics to cybersecurity.
  5. Salary potential: Computer science jobs are often high-paying, with many opportunities for career advancement.

Disadvantages of computer science:

  1. Complexity: Computer science can be a complex field that requires specialized skills and expertise.
  2. Rapidly changing technology: Computer science technology and tools are constantly evolving, requiring professionals to stay up-to-date with the latest developments.
  3. Ethical concerns: Computer science can raise ethical concerns, such as privacy violations, cybersecurity threats, and the potential for bias in algorithms.
  4. Work-life balance: Computer science jobs can be demanding, with long hours and high-pressure deadlines.
  5. Gender and diversity issues: Computer science has traditionally been a male-dominated field, with relatively low levels of diversity among professionals.

Advantages of data science:

  1. Improved decision-making: Data science can provide insights and predictions that can help organizations make better decisions.
  2. Personalization: Data science can help organizations personalize their products or services to better meet the needs of individual customers.
  3. Innovation: Data science can be used to identify new opportunities for innovation and to develop new products and services.
  4. Competitive advantage: Organizations that use data science effectively can gain a competitive advantage by making better decisions, improving efficiency, and identifying new opportunities.
  5. Cost-effective: Data science can help organizations reduce costs by identifying areas of inefficiency and optimizing processes.

Disadvantages of data science:

  1. Data quality: The accuracy and quality of the data used in data science can have a significant impact on the results obtained.
  2. Privacy concerns: The collection and use of data can raise privacy concerns, particularly if the data is personal or sensitive.
  3. Complexity: Data science can be a complex and technical field that requires specialized skills and expertise.
  4. Bias: Data science algorithms can be biased if the data used to train them is biased, which can lead to inaccurate results.
  5. Interpretation: Interpreting data science results can be challenging, particularly for non-technical stakeholders who may not understand the underlying assumptions and methods used.

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Last Updated : 14 Apr, 2023
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