Data Science

What Is the Difference Between Data Science Vs. Computer Science?

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The tech sector is quite broad, with various subsections and career opportunities. Picking one from its multiple branches can be an arduous task. However, you can determine which tech field is suitable for you by learning about the ones that suit your interests the most. Data Science and Computer Science are among the most prominent areas in the tech sector. Both of these fields are related to software applications and their use to solve business problems. Hence, they have plenty of similarities. Due to such parallels, the data science vs. computer science comparison can be quite confusing.

If you want to pick a tech career, you should know about the distinctions between these two tech fields. It would help you determine which kind of tech career you want to pursue and whether you wish to get data science jobs or computer science jobs. To help you choose between these two tech fields, we have prepared the following column. You’ll learn about the differences between data science and computer science, their highlights, their career prospects, and how you can choose between the two of them. 


Data science and computer science are both significant sectors. Computer science is a conventional subject and has been present for many decades. However, data science is relatively new and has become one of the hottest fields in the last decade. That’s because more and more companies are becoming reliant on big data and data analytics to find insights, which help them make better decisions. Professionally, data science is quite a lucrative field as statistics show that transitioning from a marketing analytics role to a data science role leads to a 37% salary increase on average. 

Data science is a significantly more in-demand and advanced field in comparison to computer science. Due to this, entering the field of data science requires a bit more effort than computer science. To become a data science professional, you will need to complete a course or degree equivalent in engineering, maths, stats, commerce, or MBA. You must be familiar with the various tools and technologies used in collecting, analyzing, and visualizing data. You must also be familiar with the different subjects present in data science, including mathematics, statistics, and computer science. You’ll need to get a data science certification to pursue a career in this field as it would equip you with the necessary skills and make you stand out from your peers for recruiters. 

1. Data Science vs. Computer Science – Overview

Data science and computer science are two of the most popular domains in the technology sector. They are both in-demand fields with many career opportunities. However, choosing between the two of these fields can be a little confusing. Both of them require professionals to work with software applications and be familiar with the technology. 

Apart from these points, data science and computer science are vastly different. Computer science studies the theory and practice of how computers operate. A computer science professional learns about programming, operating systems, software, and all the knowledge related to running a computer. Their entire focus of the study is on computer systems. 

Data science is different. It is an interdisciplinary field that focuses on working with large amounts of structured and unstructured data. It uses computer science and statistics to perform statistical analysis. A data science professional uses multiple tools and programming languages such as Hadoop, Apache Spark, R, and Python. They also use calculus, deep learning, machine learning, and databases. 

Along with this distinction, data science is focused more on applied sciences. On the other hand, computer science focuses more on the theoretical aspects. Data science applies the principles and algorithms studied in computer science. 

You may also like to read: What Is Data Science? Roles, Skills & Courses

2. Data Science vs. Computer Science – Job Opportunities

The fundamental concepts differ significantly in these domains. Data science roles focus heavily on leveraging machine learning and big data for finding insights, whereas computer science roles work on software and specific solutions. As a result, the job opportunities present in these fields also vary.

Both of these fields are laden with lucrative careers. Data science vs. computer science careers also differ in terms of their responsibilities, which we have discussed in the next section. 

Data Science vs. Computer Science - Job Opportunities

Career Opportunities in Data Science

Following are the prominent data science jobs: 

i) Data scientist

A data scientist is responsible for analyzing, processing, and modeling data. They interpret the insights generated from their analyses to create plans and help in the decision-making process. 

ii) Business intelligence developer

Business intelligence developers use data analytics and tools to find and share business-related information with the company’s stakeholders. They are responsible for creating and maintaining company business intelligence tools for which they assist in developing strategies. They have to share technical information with non-technical audiences so that they can understand them properly. 

iii) Data engineer

Data engineers prepare the Big data infrastructure that data scientists use. They are well-versed in using multiple big data technologies, including Hive, Pig, Hadoop, SQL, NoSQL, etc. Data engineers build systems that collect, store, and analyze data at scale.

Career Opportunities In Computer Science

i) Computer scientist

A computer scientist writes and programs software to develop applications. They validate and develop models that can interact between people and computers or devices. Computer scientists study and test various algorithms and applications to understand their use and potential. 

ii) Software engineer

A software engineer is responsible for developing a specific component of a software application according to the specifications provided by the designers. They work in teams and are also responsible for updating the developed product to match its users’ requirements. 

iii) Web developer

Another typical role among computer science professionals is a web developer. A web developer writes the required code to create a website. They also take care of any technical issues that arise on the website. 

3. Data Science vs. Computer Science – Skills and Responsibilities

Data science vs. computer science differences become more prominent when we cover the required skills and responsibilities for the professionals working in these domains. While choosing between data science jobs and computer science jobs, you should know which skills you’ll have to develop to pursue those roles. 

Data Science vs. Computer Science - Skills and Responsibilities

Learning about the required skills will help you clear doubts and determine which field matches your interests the most. Both of these domains are distinct and focus on different aspects of technology. That’s why the skills required are different as well. 

Data science professionals must have the following skills to succeed:

  • Data extraction
  • Data wrangling and exploration
  • Data visualization
  • Programming (SQL, R & Python)
  • Statistics
  • Machine learning
  • Deep learning
  • Big data frameworks

On the other hand, the necessary computer science skills are: 

  • Knowledge of basic computer systems
  • Data structures
  • Technical writing
  • Software development
  • Mathematics
  • Operating systems

As you can see, the difference is significant. Data science skills are more focused on working with large datasets, while computer science skills focus more on working with computers and software applications. Data science professionals must have the following skills to function: 

Analytixlabs is among the industry leaders in higher technical education in India. They have been providing high-quality data science training and courses since 2011. They have helped hundreds of students start their tech careers in reputed organizations, including Bank of America, Accenture, and Mckinsey. 

Data Science Responsibilities:

The primary responsibilities in a data science role are: 

Data mining

Data mining refers to extracting and identifying patterns in large data sets using statistics, machine learning, and database systems. As a data science professional, you’ll have to collect data from multiple sources that you choose according to their quality and reliability. You’ll also have to clean and optimize the data, so it generates accurate results. 

Analyzing data

One of the critical responsibilities of data science professionals is to analyze data for finding insights. They use various machine learning algorithms and deep learning implementations to see trends and patterns in the data. Some of the most common machine learning algorithms they use are classification, decision trees, random forest, etc. 

Sharing insights 

As a data science professional, you’ll have to share the insights you gathered from the data. Data science professionals usually share their insights with the stakeholders, such as the business leaders and investors. It requires them to be adept at storytelling and data visualization. 

Computer Science Responsibilities: 

Following are some of the most common responsibilities in computer science roles: 

Writing code

As a computer science expert, you must be familiar with multiple programming languages such as Python, C#, C++, Java, and more. You’ll have to write code, test it, and debug any issues present in it. 

Software development

Computer science professionals develop software applications that help users perform specific tasks. They develop different components of the software and combine them together to create a fully functioning application. 

Collaborating on projects

In most cases, computer science professionals work in teams, and hence, they should have strong interpersonal and communication skills. They must be familiar with different collaboration and project management software such as Zoom, Todoist, and more. 

4. How to Choose Between Data Science vs. Computer Science for your Career?

Choosing between data science vs. computer science is relatively easy if you understand both of these domains. Data science is suitable for those who like to work with numbers and statistics. Data science roles would require you to collect and analyze large quantities of data. Although it uses several aspects of computer science, data science is dependent primarily on statistics and mathematics. 

On the other hand, computer science and computer science jobs are more suitable for building things. Computer science roles are also ideal for studying the interactions between humans and these technologies. 

You should pick the domain that matches your career interests and aspirations. Do you find data exciting, or are you more interested in seeing how computers work? 

You may also like to read: Is Data Scientist an IT Job? Learn About Different Roles & Skills

5. FAQs- Frequently Asked Questions

Q1. Which is better – data science or computer science?

It depends on your preferences. Both data science and computer science have their unique highlights. Computer science focuses on researching abstract concepts related to software, computers, algorithms and is a more research-centric subject. On the other hand, data science focuses on combining the knowledge of different topics, including mathematics, statistics, and machine learning to find patterns in large amounts of data and analyze the same. 

Hence, computer science is better for those who want to study computers, their capabilities, and their work principles. Conversely, data science is better for those who want to solve business problems and find insights by using technology that would be invisible otherwise. 

You may also like to read: Data Scientist Job Description | Role of Data Scientist

Q2. Who gets paid more software engineers or data scientists? 

According to the latest statistics, data scientists earn more than software engineers. Data scientists earn a median salary of ₹8,24,384 per year while software engineers earn an average salary of ₹5,62,761 per year

Data Science vs. Computer Science - Salary

However, keep in mind that your salary pay in these fields depends on multiple factors, including your experience and skills. For example, the salary of a data scientist with less than a year’s experience is ₹5,40,000 per year on average. Similarly, the pay of a software engineer with less than a year’s experience is ₹4,14,000 per annum on average. If you’re looking for a role that pays more, then data science would be a more suitable selection. 

You may also like to read: How Do Data Scientist Salaries Fare in the Current Job Market?

Q3. Is software engineering harder than data science? 

Software engineering and data science are different domains. One is not more complex than the other. Software engineers have to know multiple programming languages such as Python and C++ and possess excellent coding skills. On the other hand, data scientists should have a deep knowledge of data analysis, mathematics, and statistics for data science. 

Software engineering is better suited for those who like to work within guidelines, while data science is more suitable for working with large amounts of data to gain insights. Both require you to study multiple subjects and learn applicable skills. 

You may also like to read: Is Data Science Hard Or Easy?

6. Concluding Thoughts

Data science vs. computer science is a long debate. However, both of them are substantially different and have their unique highlights. Data science roles usually work with data, whereas computer science roles typically work with software. Computer science is a subject of its own, while data science is an interdisciplinary field, which combines computer science with mathematics and statistics for data science

You may also like to read:

1. Top 25 Data Science Books to Learn Data Science

2. How to Become A Data Scientist – Step By Step Guide

3. Is Data Scientist an IT Job? Learn About Different Roles & Skills

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