Difference Between Data Science vs Computer Science

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Often people are confused about the difference between data science and computer science. These are interdisciplinary fields that drive IT solutions. Both branches are related to the same root of technology and computers and involve the study of programming. The knowledge and methodologies linked to both fields often overlap. For instance, each one deals with artificial intelligence and SQL databases in their daily operations. The main difference between CS and data science is:

  • Data science is a subset of computer science that includes data and its analysis. 
  • Computer science is the superset of data science and covers the field of technology.  

When trying to understand data science vs computer science, one can consider the difference in terms of benefits, real-time usage, industry, applications and academics. Here’s an overview of the two fields.

Computer ScienceData Science
Main benefit is technological advancement of devices.Main benefit is the management of data. 
Involved with the growth and development of technology.Maintain and handle big volumes of data. 
Applied to almost all companies and technical industries.Applied to companies and industries where structured and unstructured data has great importance. 
One of the oldest branches of science.It came into the limelight about 50 years ago. 
You become a dedicated computer professional after learning this domain or discipline.  You become a machine learning engineer, data engineer, data scientist, data architect or business intelligence analyst. 

Computer science enthusiasts who wish to build their career in data science can enrol into GeekLurn’s Data Science Architect Program. GeekLurn is a certified member of NASSCOM and a partner of IBM, and the program includes 20+ hours live interactive sessions with eminent data scientists and 1.5 years of research project experience. You get to work on real-time case studies, learn testing, analysis modules and NoSQL applications and deployment of Big data. You can also make the most of boot camps, cohesive peer networking and corporate specialist guidance for a successful career. After completion of the program, the institute promises 100% placement guarantee in collaboration with top HR consulting firms across India. Computer science graduates who wish to advance their careers in data science are eligible to sign up. Both freshers and working professionals can make a significant impact on their careers.

Let’s understand the difference between data science and computer science, so you can make the right choice. 

Table of Contents

What is Computer Science?

It is the study of computing concepts and computers. The field uses different algorithms to manipulate information. Computer science spans applied and theoretical research into the functional components of computers. This includes networking protocols, operating systems, hardware and software and multiple elements of information technology (IT) architecture. It also encompasses various technical concepts, like algorithm design, software engineering, human-computer interaction, programming languages and informatics and bioinformatics. Computer scientists use their skills to write codes and debug, develop and modify software applications and maximise computer systems. 

What is Data Science?

It is the process of using advanced analytics to extract meaningful insights from noisy data for business strategy planning and decision making. This is done by combining countless tools, algorithms, statistics and ML techniques. A few core roles and responsibilities of core data scientists include using SQL to query enterprise data, exploring current data and discovering new data. It also includes displaying results with visualisations (Tableau and Seaborn), comparing ML algorithms against current processes and isolating business questions and the expected impact of a model. 

Data Science vs Computer Science

The main difference between data science and computer science is in terms of computation and data. Computing is operating methods on data, and the field of studying, transforming, storing, processing and maintaining is known as data science. It mainly consists of data mining, machine learning, data analytics, data product development and exploring data insights. The hardware area of computer science includes electrical and electronics subjects, which is about computing academics and computing subjects. 

Data science involves the handling and maintenance of massive quantities of customer data. It also reduces data redundancy. Speed and performance are the main benefits of learning computer science. It uses high-end and ultra-fast devices like supercomputers. 

Data science vs computer science differs in terms of dealing with algorithms. The former is a mix of maths, statistics and data engineering, while the latter focuses on development and software engineering. 

The sub-areas of computer science are probabilistic theories, distinct structures, database design and computation, while data science sub-areas are modeling, analytics, machine learning, computational mathematics and simulation. 

Computer science may offer a detailed insight into computer machinery utilisation and applications. Data science will teach you how to extract useful information from data. 

Computer science and data science work together and each one is equally important. They have their sets of benefits in growth, development and conceptual matters. Both domains create new advanced opportunities and technologies to ease our daily lives. Data science is a computer science specialisation and can be learnt from online courses. You do not require a master’s degree, PhD or any other academic qualification. 

Neel is a Product Manager with an interest in Data Science, Machine Learning, Cloud Computing, DevOps, and Blockchain with expertise in Python, R, Java, Power BI and Data analytics.

Neel Neeraj

Neel is a Product Manager with an interest in Data Science, Machine Learning, Cloud Computing, DevOps, and Blockchain with expertise in Python, R, Java, Power BI and Data analytics.
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