What’s The Difference Between Data Science and Computer Science?

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Last Updated on August 27, 2021

As we all know, Data Science is termed as the most wanted job of the 21st century. This statement alone has such an impact, that most folks today are now interested in Simplilearn data science certification. But as we all know, some prerequisites help a data scientist stand out from others. One of those prerequisites is Computer Science skills or knowledge.

Most new learners of any data science certification course assume Data Science and Computer Science to be the same fields. In this post, we’ll go through some fundamental differences between Data Science and Computer Science. So, let’s get started.

Definition:

The study of computation and information is called Computer Science (CS). It’s a broad field of study which in general deals with the study of computer design and architecture, computation, algorithms, computational problems, design of computer systems hardware, software, networking, internet, and applications.

The core idea is to study computers and its related concepts. This study is made to apply this knowledge in other fields like science and technology, business, agriculture, etc. It has a vast number of areas to research.

Data Science (DS) is a specialized field that deals with various types of data to extract some information using multiple mathematical concepts, like statistical and descriptive methods, with the help of numerous present-day technologies. The critical intent here is to generate insights (data) from the vast amount of data available today. These insights are then used by the business to make better-informed decisions.

Scope of Field:

Computer Science covers all the technological fields. The study of computer science leads to technological advancements. Technically, it’s a superset of Data Science.

Data Science covers all studies of the data-related field. Innovations in mathematical approaches and technology lead to advancement in Data Science. Technically it’s a subset of computer science.

History:

The study of Computer Science has been in existence for many years now. It even is offered as an academic subject for research for decades.

The Data Science field though being centuries-old (in terms of studying the mathematical concepts and algorithms the Data Science uses today), has recently come to light with advancements in technology. It is now a developing branch of Science and Technology. It’s currently being offered as an academic subject for study.

Subjects:

Computer Science focuses more on topics like Algorithms, Data Structure, Programming Languages, Computer Architecture, Network Architecture, Operating Systems, etc.

Data Science focuses more on subjects like Basic and Advanced Statistics, Calculus, Data Engineering, Big Data, Machine Learning, Artificial Intelligence, etc.

End Goal / Usage / Benefits:

Technological growth and advancement are some of the benefits of the study of Computer Science. The development of efficient algorithms, applications, fast and robust systems are some of its other end goals. The study of computer science provides us with super-fast and computationally powerful systems, tools, and techniques. In the end, this is used by any end-user (Eg., Software professionals) to perform other tasks or solve real-world problems.

Computer Science fields mostly use programming languages, algorithms, or super-fast computers to solve real-world problems.

The end goal of Data Science is to get something useful out of the data. In this process, we inherently try to wrangle, inspect, and manage data. The benefit of performing data science is that we can better understand the data, i.e., it gives answers to questions like a better understanding of user behavior, purchase patterns, which product should be given more importance, etc.

The Data Science field uses large volumes of data for analysis and insights for the business.

Prerequisites:

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There are, in general, no prerequisites to study Computer Science, except for the interest in the field of study of computers. An individual with good logic building and basic computer knowledge can benefit from quick learning in computer science.

Today anyone with no relevant background or domain knowledge can start learning Data Science. To completely master it, one must know some essential calculus, statistics, and some high-level programming languages like R or Python. Along with it, interest in dealing with vast amounts of data will make you successful in the Data Science field.

Industry / Applicants:

Computer Science generally applies to all technical product or service-oriented industries and companies which make use of Information Technology (IT) or CS technologies in their business.

It is the base of the IT industries. Hence, the support for anyone who wants to be an IT/software professional. Though people from other fields are actively joining CS-based roles and profiles, people with relevant IT / CS background are preferred for the respective profiles.

The CS job may include one or a few of the following activities: Programming, Application Maintenance, Admin / Support work, System design / Architect, Desktop Support activities, etc.

Data Science generally applies to companies directly or indirectly dealing with large volumes of data. These companies have data that has one of their sources of income.

Technology giants like Google, Microsoft, or Amazon rely heavily on studying the data generated from using their services. One who aspires to be a Data Analyst or Data Scientist can explore in this field.

The DS job generally includes one or a few of the following activities: Data Cleaning, Data Wrangling / Manipulation, Model building, Big data management, and other activities.

Hurray. You made it to the last. Taking the effort to do research and clear out confusion is the first step in learning, and you have just completed the same—Pat yourself on the back.

Conclusion:

Data Analytics

In this post, we have covered some of the fundamental differences between computer science and data science fields in terms of study, history, prerequisites, usage, industry, and profession. After reading this post, we hope that most of your confusion surrounding Computer Science and Data Science must have been cleared. Hopefully, this will help all the new learners who are planning to do data science certification.

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