Difference Between Data Science Vs Computer Science

Learn about the differences and similarities between the two disciplines of Data Science vs Computer Science through this tutorial:

In this tutorial, Data science and Computer science disciplines are explained in brief. Learn about the different career options available for these disciplines to guide you in choosing the career option as per your interest.

We will compare these two disciplines and explain their differences and similarities to understand them in detail.

Computer Science vs Data Science

Data Science Vs Computer Science

Data science and computer science have a deep relationship because there are inherently large data problems that require efficient (and reliable) computation. Computer science mainly deals with development and software engineering. However, data science has the use of subjects such as maths, statistics, and computer science.

Computer Science & Data Science differences

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Data science uses computer science principles and differs from the notions of analysis and monitoring in bringing outcomes related to prediction and simulation.

Outcomes related to prediction and simulation

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>> Click here to read more about data science and its comparison with big data analytics to understand data science’s multidisciplinary nature.

Data Science makes use of machine learning and other techniques, which connect the computational issues in data science with the algorithmic matters in computer science. In other words, we can say that Computer science is used in data science to understand digital patterns in structured and unstructured data and to simplify many complex analytical tasks.

Computer science’s algorithmic approach focuses on the mathematical foundations of numerical computation and gives its practitioners the tools to create efficient algorithms and optimize their results.

In modern data science, starting with the necessary skills of algorithms and algorithmic modeling, students study the fundamentals of using various algorithms and data mining techniques. Machine learning and data science are so new and dynamic that there is no single fundamental theorem that can define it.

Comparison Of Data Science And Computer Science

Computer ScienceData Science
Study of computers, their design, architecture.
It encompasses software and hardware elements of computers, machines, and devices.
Study of data, their type, data mining, manipulation.
machine learning, prediction, visualization and simulation
Main Areas of Application
Computers
Databases
Networks
Security
Informatics
Bioinformatics
Programming languages
Software engineering
Algorithm designing
Big data analytics
Data engineering
Machine learning
Recommendation
User-behavior analysis
Customer analytics
Operational analytics
Predictive analytics
Fraud detection, etc.
Presence in Academics
Exists for many years in academicsIt has been brought recently in academics
Career Options
Application/System Developer
Web Developer
Hardware Engineer
Database Administrator
Computer Systems Analyst,
Forensic Computer Analyst,
Information Security Analyst, etc.
Data Analyst
Data Scientist
Data Engineer
Data Warehouse engineer
Business Analysts
Analytics Manager
Business Intelligence Analysts

Data Science Career Options

Finding the right job is an essential thing in the life of most individuals. However, it is quite an effort to skim through all the dissolving definitions and confusing career titles in data science.

Data Science Career Options

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Here is the list of some of the most common job titles existing in this field.

#1) Data Analyst

It is an entry-level job in data science. As a data analyst, one is given questions by the business. The data analyst has to answer those based on his skills in data mining, data visualization, probability, statistics, and the ability to present complex information in an easy-to-understand way using dashboards, graphs, charts, etc.

Suggested reading =>> Differences between Data Analyst vs Data Scientist

#2) Data Scientist

As a data scientist, and as a senior person, one needs to have appropriate experience dealing with extensive data. Some activities of a data scientist are similar to that of a data analyst. A possible addition is the skill to use machine learning. Data scientists design, develop, and evolve machine learning models to make accurate predictions based on past and real-time data.

Data scientists generally work independently to find out patterns on information that management might not have found and could do for the company’s benefit.

#3) Data Engineer

Data engineers are responsible for creating and maintaining the data analytics infrastructure and pipeline of a company by using their skills in advanced SQL, system administration, programming, and scripting skills to automate various tasks.

>> Click here to learn more about a data analyst, data scientist, and a data engineer.

Some other job titles similar to those mentioned above are Machine Learning Engineer, Quantitative Analyst, Business Intelligence Analyst, Data Warehouse Engineer, Data Warehouse Architect, Statistician, Systems Analyst, and Business Analyst.

Computer Science Career Options

On completing a computer science degree, some of the most common jobs that one might find are given below:

Career Options

#1) Applications/Systems Software Developer

Software developers are creative individuals who are responsible for designing, developing, and installing software systems. They have software development skills, version maintenance, and need to have an eye to catch small errors in a large codebase. Quality of problem-solving and resolving issues in broken code is immensely appreciated in the career of developers.

Along with the technical skills required for software development, a person also needs to communicate their findings to management and collaborate with other developers and testers.

#2) Computer Hardware Engineer

A computer system consists of two major elements, i.e., Software and Hardware.

Computer hardware engineers deal with processes of designing, testing, and producing computers and their components related to various subsystems and electronic hardware such as monitors, keyboards, motherboards, mice, USB devices, firmware OS (BIOS), and other such components as sensors and actuators.

#3) Web Developer

Web developer has the same skill sets as that of a software developer. However, they code for applications that run in the browser. It means that a web developer needs to know HTML, CSS, and JavaScript to develop front end parts of the web application.

Moreover, to develop parts of the backend that take care of interaction with the databases and the application’s business logic, one needs to know programming languages such as Perl, Python, PHP, Ruby, Java, etc. However, recently with the advent of new homogenous stacks such as NodeJS, it has become possible to write backend functionalities in JavaScript.

#4) Database Administrator

A database administrator is responsible for the running and maintenance of one or more database systems. Administrators usually have specialization in storing and processing data in databases with the help of queries, triggers, and stored procedures and packages. They need to ensure the security and availability of data to the users and other stakeholders.

After computer science, some other standard career options are Computer Systems Analyst, Forensic Computer Analyst, Information Security Analyst, etc.

Key Differences – Computer Science Vs Data Science

Some critical differences between Computer Science and Data Science are related to their scope and work roles related to these fields.

These are enlisted below:

  • Computer Science is more about software, machines, and devices. However, data science uses those aspects to bring outcomes by processing data with software and computing devices.
  • Computer Science has activities related to the development and creation of computing, storage, and networking, whereas data science has activities that relate to understanding user and organization behavior.
  • In Computer Science, one has to study computer architecture, software algorithms, hardware and software design, and implementation. However, in data science, one has to explore types of data such as structured, unstructured, and machine learning algorithms to predict and simulate future outcomes.

Recommended reading =>> Difference between Data Science, Big Data and Data Analytics

Frequently Asked Questions

Q #1) What pays more Data Science or Software Engineering?

Answer: Data Science pays more than software engineering. On average, a software engineer earns a salary of USD 100000 per annum. However, a data scientist earns an annual salary of more than USD 140000. Having data science skills can quickly increase your salary by USD 25000 to 35000 per annum if you are a software developer or an experienced systems engineer.

Q #2) Do you need computer science for Data Science?

Answer: Computer science may be necessary for data science. To be a data scientist, one might have to learn computer science. However, it is more of a subjective matter. According to Professor Haider, anyone who can articulate a story with appropriate visualization tools by drawing insights from structure or unstructured data can become a data scientist.

Q #3) Which is better Computer Science or Data Science?

Answer: Both computer science and data science are acceptable. Computer science has its relevance, and data science has its own. Both the sciences have many similarities and differences, as also highlighted in the article above. However, concerning salaries, data scientists are paid more than engineers in Computer science.

Conclusion

In this Data Science vs Computer Science article, while comparing both the sciences, we have listed down application areas and standard career options, explaining the details of engineers’ activities in each area.