Computer science VS Data science

With a rapid increase in the technology sphere, the two highest growth and demand careers are data science and computer science. Both go hand in hand. You must be wondering what differentiates them. Though they overlap with one another, each one is unique in terms of the people they attract and also offers distinct career paths. The question here is computer science vs. data science. Which is better? Which one should you choose? It is important to know the differences and what it takes to excel in both before jumping to a conclusion. Read the article below to know the core differences, advantages, roles, responsibilities, real-world application, and benefits. This will help you to make a decision that is right for you! This article will help guide you through the following:

Computer science VS Data science - Table of Content

What is Data Science?

Data science has now emerged as an independent discipline, thereby becoming the buzzword in the tech world. With an increase in the internet and the use of sensors in almost everything from planes to bulbs, immense data is being produced every day. This data can be used to identify patterns and build accurate algorithms to build predictive models to improve business strategies and operations. The whole process of business can become more efficient and productive with the help of technology. The process of data science is “set in stone” similar to the scientific process. Most data scientists use tools like Python, Jupyter Notebook, or SQL because they can be applied to any business. Therefore, a data scientist’s role is not limited to an industry or sector. 

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What is Computer Science?

As the name implies, computer science is the study of computers and also computation systems. Computer engineers mostly deal with software and software systems, which includes design, development,  and also its application. The areas of study in computer science include database systems, programming languages, graphics, human-computer interactions, computer systems, software engineering, artificial intelligence, and others. Though one should know how to program in the study of computer science, it is considered only one element of the field. A computer scientist studies the performance of both computer hardware and software by designing and analysing algorithms to solve programs. Therefore, a computer scientist can explore various roles in different sectors and there is no limit to it.

So you know what exactly each technology is about ! Let us explore further.

Key Differences Between Data Scientist and Computer Science

Data science and computers are both trending technologies. Many organisations, such as Tech Mahindra, Infosys, Wipro, and others have both data scientists as well as computer scientists. Let us understand the difference between the two.

  • On one hand, Data science focuses more on machine learning algorithms, on the other hand, computer science focuses on software design.
  • A data Scientist has a background in statistics, while a computer scientist comes from a computer engineering background.
  • Both the degrees, i.e. data science, and computer science, are different from one another. It is generally a data science or computer science degree.
  • A computer scientist's role is more automated and object-oriented. Whereas, a data scientist works with product managers or is aligned with other business-facing roles.
  • As a computer scientist, one can get into a variety of roles as compared to a data scientist. 

Though both the technologies, data science, and computer science, fall under the same umbrella of technology, they both have unique features, uses roles, and responsibilities.

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Data Science vs Computer Science

Data science is a specific study of knowledge within the study of computers that focuses on analytics, statistics, and programming. Computer science refers to the study of building hardware and programming software. Therefore, data scientists need to have skills so that they can collect and also analyse the data. Whereas, a computer scientist on the other hand is responsible for building the new technologies.  

An aspiring computer scientist should have a bachelor's degree in the field of computer science. But, to land a leadership role, a data scientist will have to pursue a master's degree also. A data scientist should have skills such as programming, statistics, and mathematics along with a strong academic background. A computer scientist should have skills in creating new computing languages, testing new systems and designing them, and developing and simplifying algorithms.

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Thus, both data science and computer science are leveraged for different purposes in an organisation. Both technologies help in making business processes more efficient. 

1. Roles and Responsibilities

A data scientist helps in strategizing business processes and improving their efficiency. Whereas a computer scientist helps in solving complex problems in the organisation using technical solutions.

Data Science

  • Extracting valuable data from various data sources
  • Use the tools of machine learning to preprocess both structured and unstructured data.
  • Analyse huge datasets to find out trends and patterns.
  • Help leaders present data using visualisation techniques.
  • Explore data tools to figure out innovative data strategies.
  • Develop predictive models along with machine learning algorithms to help businesses take strategic decision
  • Assist in building data engineering pipeline
  • Collaborate with both the business and Information technology teams.
  • Stay updated with new tools and trends in technology.
  • Guide peers to use the data effectively by conducting knowledge sharing sessions on a timely basis 

Computer Science

  • To meet the changing requirements, computer scientists need to develop and also maintain analytical procedures.
  • Conduct research to evaluate the technical performance of software products by working as an expert.
  • Participate by constantly giving feedback for design review components (hardware and software) to make sure that they apply to the current system and requirements.
  • Deliver presentations’, recommendations, high-quality papers, and findings for senior officials in the organisation.
  • Evaluate the performances using the technical performance measures. 

Thus, each technology has unique roles and responsibilities and helps businesses grow by making the best use of it.


2. Skills Required

A data scientist needs to have both technical and non-technical skills. Technical skills such as Maths, Statistics, Analytics, Machine Learning methods, Modelling, Programming, Data Visualisation, Big Data, Deep Learning, Computing Skills, and Data Visualisation such as Matplotlib, Tableau, and others. Non-technical skills such as communication, business acumen, and intellectual curiosity.

A computer scientist should have both technical and soft skills to excel in the job. Technical skills such as computer & technology skills, software development, data analysis, mathematics, and technical writing are required. Soft skills such as creativity, time management, attention to detail, teamwork & cooperation, organising, analysis, and others are required to excel in a career as a computer scientist.

As seen, the skills required for both technologies are different even though they are assumed to be similar. If you explore one of the technologies as a career path, you need to develop a different set of skills for each. 

3. Career Opportunities

One of the main aspects of choosing a career is to understand what opportunities it offers in the long run and also how you can explore the career ladder. Below is the list of career opportunities that each technology offers -

  • Data Science

A data science aspirant has a plethora of opportunities as the demand is increasing.  A few of the career opportunities that you can explore in data science are as follows:

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A. Data Analyst

Most data scientists begin their careers as data analysts or data engineers. Analysts work with raw data, which is collected through the system. They are required to work with various teams such as finance, marketing, and sales to process information. The responsibility of a data analyst is to clean the data, study, and create different reports using visualisation tools such as Tableau and Excel. This will help teams develop strategies.

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B.  Administrators and Data Architects 

The primary work is to understand the strategy and data that should be selected. Their job is to enhance the performance or create a new system of databases. In a nutshell, data engineers build the infrastructure while data architects design the flow and process for data management.

C.  Data Engineer

A  data engineer is an expert who accesses and processes huge data and that also in real-time. They are required to interpret unformatted and unverified data. Therefore, one of their tasks is to maintain data accessible for volumes of data high in nature as well as create pipelines to ensure data is accessible for further analysis with all the data teams. 

D.  Machine Learning Engineer

A unique combination of both software and data science can be termed Machine learning engineers as they work with big data daily. In a few organisations, both work together while some have independent responsibilities. Data scientists are generally expected to be machine learning experts along with advanced programming skills. Many years of experience and expertise are required to be a machine learning engineer. Hence, they are typically employed in senior/management roles.

E.  Data Scientist

The job of a data scientist is to analyse big data to address real business problems. The management relies on data scientists to provide trends, patterns, and actionable insights that help in framing strategies in business.

F.   Business/Marketing Analyst

and also analyses industry market trends along with the industry. The job of an analyst here is to process large amounts of data and opportunities to improve the revenues and growth in business. Whereas, a marketing analyst needs to identify shifting consumer behaviour and find out new buying trends.  Since most of the businesses are online a huge amount of data is processed to create all the go-to0 market strategies.

  • Computer science

Similar to data science, a computer scientist also has a vast variety in choosing career opportunities. Below mentioned are a few of them:


A.  Software Developer

One of the most popular career choices for a computer scientist is software development. They create software programs that permit users to perform certain tasks on devices such as computers or mobile devices. A software engineer is responsible for the entire development. Along with technical skills to solve problems a software developer should have communication skills.

B.  Web Developer

As the name implies web developers create the technical structure for the websites and ensure that the web pages are easily accessible and easily downloadable. They evaluate the need for information-based resources.  Web developers use search engine optimization to maximise the number of page views and visitors.

C.  Computer Hardware engineer

A computer hardware engineer is responsible to design, develop and test all the computer components such as routers, memory devices, and circuit boards. They should have both technical and technical expertise. To make sure the hardware is functioning, engineers need to have the perseverance to perform comprehensive tests. Therefore, a computer hardware engineer should be an avid learner to stay updated on the top emerging trends, so that the latest programs and applications can be accommodated. 

D.  Computer Network Architect

The computer network architect is responsible for designing, implementing, and maintaining network and data communication systems, including networks of the local area, network area, extranets, wide area, and intranets. They test systems so that problems can be detected and solved before they are implemented. They also evaluate the need for data sharing and communications.  A computer network architect needs to have analytical skills to evaluate computer networks.

E.   IT project Manager

A project manager in the IT sector is responsible for completing the project by coordinating the efforts of a team of all the programmer’s/developers. A manager needs to have a broad knowledge of computer systems and technology along with problem-solving skills to excel in the role.  Apart from the tech skills, strong communication skill is required to understand the needs of a user and communicate the same to developers.

Computer and Information systems manager, Information security analyst, Computer system analyst, and Database Administrator are some of the roles/ career opportunities that a computer scientist can explore along with the ones mentioned above.

4. Concepts 
  • Data Science

The fundamental concepts of data science are as follows –

1) Machine learning - It is used to build predictive models. It is a branch of artificial intelligence that programs a system to automatically perform a specified task.

2)  Algorithms - It is a set of rules or processes that is used in the calculation to perform a task or solve a problem.

3)  Programming - Programming is used to develop and build models which are used in Data analysis.

4)  Statistic Model - It is the process of analysing large data sets by representing the data using mathematics so that one can make inferences from the sample provided.

5)  Regression Analysis - It is used for statistical modelling in data science to figure trends in the data to predict or forecast any specific behaviours.  

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  • Computer Science

The fundamental concepts of computer science are as follows -

  1. Computer programming
  2. Operating systems
  3. Computer organisation
  4. Data Structure and Algorithms 

Some  of the  Courses for Computer Science

  1. Android N developer
  2. Redux and React
  3. Angular 6
  4. Google cloud platform Architecture
  5. Web development or full stack developer
  6. R and software development
  7. Fundamentals of Java Programming
  8. iOS 11 and swift developer
     
5.   Real World Applications

Data science is filled with wonderful applications.  It is helping businesses to achieve revenue and business growth. Data science can be used in all sectors and industries. From logistics to medical. In all the areas where data can be leveraged for effective decision making, data science can be applied to reach conclusions and take decisions.  It is a game changer. Data science can be applied to education, logistics, airline planning, internet search, digital marketing, and others.

Computer science also assists a plethora of activities in business. For instance, in medicine, it is used to maintain databases, diagnoses, X- rays, and others.  In the Entertainment industry, it helps you watch movies online. Managing inventory, creating virtual sample products, video conferencing, and others.  Thus, the use of computer science can be applied to all the verticals and sectors in the economy from education to government, banking, home, and business.

[Related Article: What is Data Science]


6.   Salaries
  • Data Science

As per Glass door, the annual salary of a data scientist is approximately Rs 11lacs annually. It starts from 4 lacs and varies depending on your skills and experience.

  • Computer science

As per upgrade, computer science professionals with 0 to 4 years of experience can make around 14 lacs annually. It will increase as you gain experience.

Salaries will differ from one organisation to another and also depend on the skills of the individual. As compared to the previous year salaries of data science roles have significantly increased witnessing a growth of 25% on an average. As per the Bureau of Labour Statistics, the salary of a computer scientist will grow 15%  from 2019 to 2029 as the demand keeps increasing. Therefore both the technologies in terms of remuneration offer great career paths in monetary terms also.

7.  Tools and languages
  • Data Science Tools

Tools such as SAS, BigML, and D3. Js, MATLAB, Apache Spark, Ggplot 2, Jupyter, Tableau, Matplotlib, Tensor Flow, Scikit – learn languages such as python, scala, R, SQL, C/C++, Java, and Java script are required.

  • Computer Science Tools

Github, Cloud9 IDE,  Gleek,  Codepa, Buddy, Atom, Chrome Devtools, HTML5 builder, Vim, Azure, Stack overflow, Docker, Zoho Creator, Visual code studio, JIRA, Raygun, LXC, and others are some of the tools that a computer scientist needs to know. Programming languages such as C, C++, Java, Java Script, Php, Ruby, Objective C, and Python are some of the languages that are required to excel in a career as a computer scientist.


8. Benefits 

The benefits of using both technologies are numerous. Below is a brief idea of how both data science and computer science help in increasing the revenue in business.

  • Data Science

As you must have understood, data science is all about analyzing large data sets to derive a conclusion for making business decisions. Thus, it improves business predictions, and business intelligence, Increases information security, automates the recruitment process, Complex data interpretation, and others.  Data science can be applied in all businesses across verticals to make processes more efficient and thereby increase the revenue of the company.

  • Computer Science

Computer science has been a blessing for ages. The ability to forecast, Increased connectivity, Education training, e-meeting, and Collaboration opportunities are some of the benefits of using the technology in business. It depends on how much they can leverage the use of technology to derive the best results.

Conclusion 

Data science VS Computer science, which is better could be difficult to conclude. As you must have seen, the roles and responsibilities are different. Both have different career paths by falling under the same umbrella of technology. The objective of data science is to solve complex business problems by using machine learning algorithms whereas, computer science aims at directing object-oriented programming or software engineering or could be more information technology having a general knowledge of everything. It all depends on one’s passion and interest. Comment below if you have any queries to clarify. 

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Gayathri
Gayathri
Research Analyst
As a senior Technical Content Writer for HKR Trainings, Gayathri has a good comprehension of the present technical innovations, which incorporates perspectives like Business Intelligence and Analytics. She conveys advanced technical ideas precisely and vividly, as conceivable to the target group, guaranteeing that the content is available to clients. She writes qualitative content in the field of Data Warehousing & ETL, Big Data Analytics, and ERP Tools. Connect me on LinkedIn.

The answer depends on the individual. It is really difficult to interpret which one you prefer. It all depends on the interest and might differ from person to person.

You can pursue a course from our Institute HKR training.  One of the leading institutes that have helped individuals like you shape their career.

Various skills such as data analysis, technical writing, and software development along with good communication skills are required to be a computer scientist.